def get_color_map(self, levels):
"""Returns gradient of color from green to red.
"""
sm = ScalarMappable(cmap='RdYlGn_r')
normed_levels = levels / np.max(levels)
colors = 255 * sm.to_rgba(normed_levels)[:, :3]
return ['#%02x%02x%02x' % (r, g, b) for r,g,b in colors]
class ColorbarWidget(QWidget):
def __init__(self, parent=None):
super(ColorbarWidget, self).__init__(parent)
fig = Figure()
rect = 0.25, 0.05, 0.1, 0.90
self.cb_axes = fig.add_axes(rect)
self.canvas = FigureCanvas(fig)
self.setLayout(QVBoxLayout())
self.layout().addWidget(self.canvas)
self.button = QPushButton("Update")
self.layout().addWidget(self.button)
self.button.pressed.connect(self._update_cb_scale)
self._create_colorbar(fig)
def _create_colorbar(self, fig):
self.mappable = ScalarMappable(norm=SymLogNorm(0.0001, 1,vmin=-10., vmax=10000.),
cmap=DEFAULT_CMAP)
self.mappable.set_array([])
fig.colorbar(self.mappable, ax=self.cb_axes, cax=self.cb_axes)
def _update_cb_scale(self):
self.mappable.colorbar.remove()
rect = 0.25, 0.05, 0.1, 0.90
self.cb_axes = self.canvas.figure.add_axes(rect)
self.mappable = ScalarMappable(Normalize(30, 4300),
cmap=DEFAULT_CMAP)
self.mappable.set_array([])
self.canvas.figure.colorbar(self.mappable, ax=self.cb_axes, cax=self.cb_axes)
self.canvas.draw()
def make_graph(data):
from matplotlib.cm import jet, ScalarMappable
from matplotlib.colors import Normalize
g = nx.Graph()
cnorm = Normalize(vmin=1, vmax=241)
smap = ScalarMappable(norm=cnorm, cmap=jet)
edge_list = []
for k in data:
tk = k.split('_')
if len(tk) != 5:
g.add_node(k)
else:
a,b = tk[1],tk[3]
g.add_node(a)
g.add_node(b)
g.add_edge(a,b)
pos = nx.spring_layout(g)
nxcols,glabels = [],{}
for i,node in enumerate(g.nodes()):
if '_' not in node:
nxcols.append(smap.to_rgba(int(node)))
else:
nxcols.append((0,1,0,0))
nx.draw_networkx_nodes(g,pos,node_color=nxcols)
nx.draw_networkx_labels(g,pos)
nx.draw_networkx_edges(g,pos)
plt.show()
return
def digit_to_rgb(X, scaling=3, shape = (), cmap = 'binary'):
'''
Takes as input an intensity array and produces a rgb image due to some color map
Parameters
----------
X : numpy.ndarray
intensity matrix as array of shape [M x N]
scaling : int
optional. positive integer value > 0
shape: tuple or list of its , length = 2
optional. if not given, X is reshaped to be square.
cmap : str
name of color map of choice. default is 'binary'
Returns
-------
image : numpy.ndarray
three-dimensional array of shape [scaling*H x scaling*W x 3] , where H*W == M*N
'''
sm = ScalarMappable(cmap = cmap)
image = sm.to_rgba(enlarge_image(vec2im(X,shape), scaling))[:,:,0:3]
return image
def create_heatmap(xs, ys, imageSize, blobSize, cmap):
blob = Image.new('RGBA', (blobSize * 2, blobSize * 2), '#000000')
blob.putalpha(0)
colour = 255 / int(math.sqrt(len(xs)))
draw = ImageDraw.Draw(blob)
draw.ellipse((blobSize / 2, blobSize / 2, blobSize * 1.5, blobSize * 1.5),
fill=(colour, colour, colour))
blob = blob.filter(ImageFilter.GaussianBlur(radius=blobSize / 2))
heat = Image.new('RGBA', (imageSize, imageSize), '#000000')
heat.putalpha(0)
xScale = float(imageSize - 1) / (max(xs) - min(xs))
yScale = float(imageSize - 1) / (min(ys) - max(ys))
xOff = min(xs)
yOff = max(ys)
for i in range(len(xs)):
xPos = int((xs[i] - xOff) * xScale)
yPos = int((ys[i] - yOff) * yScale)
blobLoc = Image.new('RGBA', (imageSize, imageSize), '#000000')
blobLoc.putalpha(0)
blobLoc.paste(blob, (xPos - blobSize, yPos - blobSize), blob)
heat = ImageChops.add(heat, blobLoc)
norm = Normalize(vmin=min(min(heat.getdata())),
vmax=max(max(heat.getdata())))
sm = ScalarMappable(norm, cmap)
heatArray = pil_to_array(heat)
rgba = sm.to_rgba(heatArray[:, :, 0], bytes=True)
rgba[:, :, 3] = heatArray[:, :, 3]
coloured = Image.fromarray(rgba, 'RGBA')
return coloured
def scatter3d(x,y,z, cs, colorsMap='jet'):
cm = plt.get_cmap(colorsMap)
cNorm = Normalize(vmin=min(cs), vmax=max(cs))
scalarMap = ScalarMappable(norm=cNorm, cmap=cm)
ax.scatter(x, y, z, c=scalarMap.to_rgba(cs), s=5, linewidth=0)
scalarMap.set_array(cs)
plt.show()
def getcolor(self, V, F):
dS = numpy.empty(len(F))
for i, f in enumerate(F):
v = V[f][0]
dS[i] = v[0] * v[0] + v[1] * v[1] + v[2] * v[2]
cmap = ScalarMappable(cmap='jet')
cmap.set_array(dS)
return cmap, cmap.to_rgba(dS)
def _check_cmap_rgb_vals(vals, cmap, vmin=0, vmax=1):
"""Helper function to check RGB values of color images"""
from matplotlib.colors import Normalize
from matplotlib.cm import ScalarMappable
norm = Normalize(vmin, vmax)
sm = ScalarMappable(norm=norm, cmap=cmap)
for val, rgb_expected in vals:
rgb_actual = sm.to_rgba(val)[:-1]
assert_allclose(rgb_actual, rgb_expected, atol=1e-5)
def plot_events(mapobj,axisobj,catalog,label= None, color='depth', pretty = False, colormap=None,
llat = -90, ulat = 90, llon = -180, ulon = 180, figsize=(16,24),
par_range = (-90., 120., 30.), mer_range = (0., 360., 60.),
showHour = False, M_above = 0.0, location = 'World', min_size=1, max_size=8,**kwargs):
'''Simplified version of plot_event'''
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
from matplotlib.cm import ScalarMappable
lats, lons, mags, times, labels, colors = get_event_info(catalog, M_above, llat, ulat, llon, ulon, color, label)
min_color = min(colors)
max_color = max(colors)
if colormap is None:
if color == "date":
colormap = plt.get_cmap()
else:
# Choose green->yellow->red for the depth encoding.
colormap = plt.get_cmap("RdYlGn_r")
scal_map = ScalarMappable(norm=Normalize(min_color, max_color),
cmap=colormap)
scal_map.set_array(np.linspace(0, 1, 1))
x, y = mapobj(lons, lats)
min_mag = 0
max_mag = 10
if len(mags) > 1:
frac = [(_i - min_mag) / (max_mag - min_mag) for _i in mags]
magnitude_size = [(_i * (max_size - min_size)) ** 2 for _i in frac]
#magnitude_size = [(_i * min_size) for _i in mags]
#print magnitude_size
colors_plot = [scal_map.to_rgba(c) for c in colors]
else:
magnitude_size = 15.0 ** 2
colors_plot = "red"
quakes = mapobj.scatter(x, y, marker='o', s=magnitude_size, c=colors_plot, zorder=10)
#mapobj.drawmapboundary(fill_color='aqua')
#mapobj.drawparallels(np.arange(-90,90,30),labels=[1,0,0,0])
#mapobj.drawmeridians(np.arange(mapobj.lonmin,mapobj.lonmax+30,60),labels=[0,0,0,1])
# if len(mags) > 1:
# cb = mpl.colorbar.ColorbarBase(ax=axisobj, cmap=colormap, orientation='vertical')
# cb.set_ticks([0, 0.25, 0.5, 0.75, 1.0])
# color_range = max_color - min_color
# cb.set_ticklabels([_i.strftime('%Y-%b-%d') if color == "date" else '%.1fkm' % (_i)
# for _i in [min_color, min_color + color_range * 0.25,
# min_color + color_range * 0.50,
# min_color + color_range * 0.75, max_color]])
return quakes
def _create_colorbar(self, cmap, ncolors, labels, **kwargs):
norm = BoundaryNorm(range(0, ncolors), cmap.N)
mappable = ScalarMappable(cmap=cmap, norm=norm)
mappable.set_array([])
mappable.set_clim(-0.5, ncolors+0.5)
colorbar = plt.colorbar(mappable, **kwargs)
colorbar.set_ticks(np.linspace(0, ncolors, ncolors+1)+0.5)
colorbar.set_ticklabels(range(0, ncolors))
colorbar.set_ticklabels(labels)
return colorbar
def cm2colors(N, cmap='autumn'):
"""Takes N evenly spaced colors out of cmap and returns a
list of rgb values"""
values = range(N)
cNorm = Normalize(vmin=0, vmax=N-1)
scalarMap = ScalarMappable(norm=cNorm, cmap=cmap)
colors = []
for i in xrange(N):
colors.append(scalarMap.to_rgba(values[i]))
return colors
def plotTciData(self, ax):
rdata = self.elecTree.getNode('\electrons::top.tci.results:rad').data()
sm = ScalarMappable()
rhoColor = sm.to_rgba(-rdata)
for i in range(1,11):
pnode = self.elecTree.getNode('\electrons::top.tci.results:nl_%02d' % i)
ax.plot(pnode.dim_of().data(), pnode.data(), c = rhoColor[i-1])
ax.set_ylabel('ne')
def plot_net_layerwise(net, x_spacing=5, y_spacing=10, colors=[], use_labels=True, ax=None, cmap='gist_heat', cbar=False, positions={}):
if not colors:
colors = [1] * net.size()
args = {
'ax' : ax,
'node_color' : colors,
'nodelist' : net.nodes(), # ensure that same order is used throughout for parallel data like colors
'vmin' : 0,
'vmax' : 1,
'cmap' : cmap
}
if not positions:
# compute layer-wise positions of nodes (distance from roots)
nodes_by_layer = defaultdict(lambda: [])
def add_to_layer(n,l):
nodes_by_layer[l].append(n)
net.bfs_traverse(net.get_roots(), add_to_layer)
positions = {}
for l, nodes in nodes_by_layer.iteritems():
y = -l*y_spacing
# reorder layer lexicographically
nodes.sort(key=lambda n: n.get_name())
width = (len(nodes)-1) * x_spacing
for i,n in enumerate(nodes):
x = x_spacing*i - width/2
positions[n] = (x,y)
args['pos'] = positions
if use_labels:
labels = {n:n.get_name() for n in net.iter_nodes()}
args['labels'] = labels
if ax is None:
ax = plt.figure().add_subplot(1,1,1)
nxg = net_to_digraph(net)
nx.draw_networkx(nxg, **args)
ax.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='off')
ax.tick_params(axis='y', which='both', left='off', right='off', labelleft='off')
if cbar:
color_map = ScalarMappable(cmap=cmap)
color_map.set_clim(vmin=0, vmax=1)
color_map.set_array(np.array([0,1]))
plt.colorbar(color_map, ax=ax)
ax.set_aspect('equal')
# zoom out slightly to avoid cropping issues with nodes
xl = ax.get_xlim()
yl = ax.get_ylim()
ax.set_xlim(xl[0]-x_spacing/2, xl[1]+x_spacing/2)
ax.set_ylim(yl[0]-y_spacing/2, yl[1]+y_spacing/2)
def _update_scatter(self):
# Updates the scatter points for changes in *tidx* or *xidx*. This
# just changes the face color
#
# CALL THIS AFTER *_update_image*
#
if len(self.data_sets) < 2:
return
sm = ScalarMappable(norm=self.cbar.norm,cmap=self.cbar.get_cmap())
colors = sm.to_rgba(self.data_sets[1][self.tidx,:,2])
self.scatter.set_facecolors(colors)
def densityplot2(self,modelname='Model',refname='Ref',units = 'mmol m-3',sub = 'med'):
'''
opectool like density plot
Ref is in x axis
Model in y axis
Args
- *modelname* (optional) string , default ='Model'
- *refname* (optional) string , default ='Ref'
- *units* (optional) string , default ='mmol m-3'
- *sub* (optional) string , default ='med'
Returns: a matplotlib Figure object and a matplotlib Axes object
'''
fig, ax = plt.subplots()
plt.title('%s Density plot of %s and %s\nNumber of considered matchups: %s' % (sub, modelname, refname, self.number()))
cmap = 'spectral_r'
axis_min = min(self.Ref.min(),self.Model.min())
axis_max = max(self.Ref.max(),self.Model.max())
extent = [axis_min, axis_max, axis_min, axis_max]
hexbin = ax.hexbin(self.Ref, self.Model, bins=None, extent=extent, cmap=cmap)
data = hexbin.get_array().astype(np.int32)
MAX = data.max()
for nticks in range(10,2,-1):
float_array=np.linspace(0,MAX,nticks)
int___array = float_array.astype(np.int32)
if np.all(float_array == int___array ):
break
mappable = ScalarMappable(cmap=cmap)
mappable.set_array(data)
#fig.colorbar(mappable, ticks = int___array, ax=ax)
cbar = fig.colorbar(mappable, ax=ax)
labels = cbar.ax.get_yticklabels()
FloatNumberFlag = False
for label in labels:
numstr = str(label.get_text())
if numstr.find(".") > -1:
FloatNumberFlag = True
if FloatNumberFlag:
cbar.remove()
cbar = fig.colorbar(mappable, ticks = int___array, ax=ax)
ax.set_xlabel('%s %s' % (refname, units))
ax.set_ylabel('%s %s' % (modelname,units))
ax.grid()
return fig,ax
def generalized_bar_chart(code_matrix, trans_names, code_names, show_it=True, show_trans_names=False, color_map = "jet", legend_labels = None, title=None, horizontal_grid = True):
ldata = {}
fig = pylab.figure(facecolor="white", figsize=(12, 4))
fig.subplots_adjust(left=.05, bottom=.15, right=.98, top=.95)
code_names = [c for c in range(code_matrix.shape[1])]
for i, code in enumerate(range(len(code_names))):
ldata[code] = [code_matrix[j, i] for j in range(len(trans_names))]
ind = np.arange(len(trans_names))
width = 1.0 / (len(code_names) + 1)
traditional_colors = ['red', 'orange', 'yellow', 'green', 'blue', 'purple', 'black', 'grey', 'cyan', 'coral']
ax = fig.add_subplot(111)
if title is not None:
ax.set_title(title, fontsize=10)
if color_map == "AA_traditional_":
lcolors = traditional_colors
else:
cNorm = mpl_Normalize(vmin = 1, vmax = len(code_names))
comap = get_cmap(color_map)
scalar_map = ScalarMappable(norm = cNorm, cmap = comap)
lcolors = [scalar_map.to_rgba(idx + 1) for idx in range(len(code_names))]
the_bars = []
for c in range(len(code_names)):
new_bars = ax.bar(ind + (c + .5) * width, ldata[code_names[c]], width, color=lcolors[c % (len(lcolors))])
the_bars.append(new_bars[0])
# bar_groups.append(bars)
if show_trans_names:
ax.set_xticks(ind + .5)
ax.set_xticklabels(trans_names, size="x-small", rotation= -45)
else:
ax.grid(b = horizontal_grid, which = "major", axis = 'y')
ax.set_xticks(ind + .5)
ax.set_xticklabels(ind + 1, size="x-small")
for i in ind[1:]:
ax.axvline(x = i, linestyle = "--", linewidth = .25, color = 'black')
if legend_labels != None:
fontP =FontProperties()
fontP.set_size('small')
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.825, box.height])
# Put a legend to the right of the current axis
ax.legend(the_bars, legend_labels, loc='center left', bbox_to_anchor=(1, 0.5), prop = fontP)
ax.set_xlim(right=len(trans_names))
if show_it:
fig.show()
return fig
def plot(countries,values,label='',clim=None,verbose=False):
"""
Usage: worldmap.plot(countries, values [, label] [, clim])
"""
countries_shp = shpreader.natural_earth(resolution='110m',category='cultural',
name='admin_0_countries')
## Create a plot
fig = plt.figure()
ax = plt.axes(projection=ccrs.PlateCarree())
## Create a colormap
cmap = plt.get_cmap('RdYlGn_r')
if clim:
vmin = clim[0]
vmax = clim[1]
else:
val = values[np.isfinite(values)]
mean = val.mean()
std = val.std()
vmin = mean-2*std
vmax = mean+2*std
norm = Normalize(vmin=vmin,vmax=vmax)
smap = ScalarMappable(norm=norm,cmap=cmap)
ax2 = fig.add_axes([0.3, 0.18, 0.4, 0.03])
cbar = ColorbarBase(ax2,cmap=cmap,norm=norm,orientation='horizontal')
cbar.set_label(label)
## Add countries to the map
for country in shpreader.Reader(countries_shp).records():
countrycode = country.attributes['adm0_a3']
countryname = country.attributes['name_long']
## Check for country code consistency
if countrycode == 'SDS': #South Sudan
countrycode = 'SSD'
elif countrycode == 'ROU': #Romania
countrycode = 'ROM'
elif countrycode == 'COD': #Dem. Rep. Congo
countrycode = 'ZAR'
elif countrycode == 'KOS': #Kosovo
countrycode = 'KSV'
if countrycode in countries:
val = values[countries==countrycode]
if np.isfinite(val):
color = smap.to_rgba(val)
else:
color = 'grey'
else:
color = 'w'
if verbose:
print("No data available for "+countrycode+": "+countryname)
ax.add_geometries(country.geometry,ccrs.PlateCarree(),facecolor=color,label=countryname)
plt.show()
def custom_colorbar(cmap, ncolors, breaks, **kwargs):
from matplotlib.colors import BoundaryNorm
from matplotlib.cm import ScalarMappable
import matplotlib.colors as mplc
breaklabels = ['No Counts']+["> %d counts"%(perc) for perc in breaks[:-1]]
norm = BoundaryNorm(range(0, ncolors), cmap.N)
mappable = ScalarMappable(cmap=cmap, norm=norm)
mappable.set_array([])
mappable.set_clim(-0.5, ncolors+0.5)
colorbar = plt.colorbar(mappable, **kwargs)
colorbar.set_ticks(np.linspace(0, ncolors, ncolors+1)+0.5)
colorbar.set_ticklabels(range(0, ncolors))
colorbar.set_ticklabels(breaklabels)
return colorbar
def generate_colors(desired_palette, num_desired_colors):
"""
Generate an array of color strings, interpolated from the desired_palette.
desired_palette is from palettable
Conceptually, this takes a list of colors, and lets you generate any length
of colors from that array.
"""
cmap = desired_palette.mpl_colormap
mappable = ScalarMappable(norm=Normalize(vmin=0, vmax=1), cmap=cmap)
cols = []
for i in range(1,num_desired_colors+1):
(r,g,b,a) = mappable.to_rgba((i - 1) / num_desired_colors)
cols.append(rgb_to_hex(map(lambda x: int(x*255), [r,g,b])))
return cols
def _init_scatter(self):
# Plots the scatter points at the base of each vector showing the
# vertical deformation for the second data set. If there is only
# one data set then this function does nothing.
#
# CALL THIS AFTER *_init_image*
#
if len(self.data_sets) < 2:
self.scatter = None
return
sm = ScalarMappable(norm=self.cbar.norm,cmap=self.cbar.get_cmap())
# use scatter points to show z for second data set
colors = sm.to_rgba(self.data_sets[1][self.tidx,:,2])
self.scatter = self.map_ax.scatter(self.x[:,0],self.x[:,1],
c=colors,s=self.scatter_size,
zorder=2,edgecolor=self.colors[1])
def _update_cb_scale(self):
self.mappable.colorbar.remove()
rect = 0.25, 0.05, 0.1, 0.90
self.cb_axes = self.canvas.figure.add_axes(rect)
self.mappable = ScalarMappable(Normalize(30, 4300),
cmap=DEFAULT_CMAP)
self.mappable.set_array([])
self.canvas.figure.colorbar(self.mappable, ax=self.cb_axes, cax=self.cb_axes)
self.canvas.draw()
def set_data(self, x_data, y_data, axis_min, axis_max, matchup_count, log):
logging.debug('Creating density plot...')
cmap = 'spectral_r'
extent = [axis_min, axis_max, axis_min, axis_max]
if log:
bin_spec = 'log'
else:
bin_spec = None
hexbin = self.ax.hexbin(x_data, y_data, bins=bin_spec, extent=extent, cmap=cmap)
data = hexbin.get_array()
mappable = ScalarMappable(cmap=cmap)
mappable.set_array(data)
self.fig.colorbar(mappable, ax=self.ax)
logging.debug('...success!')
self.update_title(matchup_count)
def custom_colorbar(cmap, ncolors, labels, **kwargs):
"""Create a custom, discretized colorbar with correctly formatted/aligned labels.
cmap: the matplotlib colormap object you plan on using for your graph
ncolors: (int) the number of discrete colors available
labels: the list of labels for the colorbar. Should be the same length as ncolors.
"""
from matplotlib.colors import BoundaryNorm
from matplotlib.cm import ScalarMappable
norm = BoundaryNorm(range(0, ncolors), cmap.N)
mappable = ScalarMappable(cmap=cmap, norm=norm)
mappable.set_array([])
mappable.set_clim(-0.5, ncolors+0.5)
colorbar = plt.colorbar(mappable, **kwargs)
colorbar.set_ticks(np.linspace(0, ncolors, ncolors+1)+0.5)
colorbar.set_ticklabels(range(0, ncolors))
colorbar.set_ticklabels(labels)
return colorbar
def __plot_variance(self):
pointsMin = self.__calc_min()
pointsMax = self.__calc_max()
polys = []
variance = []
varMin = 1000
varMax = 0
lastX = None
lastYMin = None
lastYMax = None
for x in pointsMin.iterkeys():
if lastX is None:
lastX = x
if lastYMin is None:
lastYMin = pointsMin[x]
if lastYMax is None:
lastYMax = pointsMax[x]
polys.append([[x, pointsMin[x]],
[x, pointsMax[x]],
[lastX, lastYMax],
[lastX, lastYMin],
[x, pointsMin[x]]])
lastX = x
lastYMin = pointsMin[x]
lastYMax = pointsMax[x]
var = pointsMax[x] - pointsMin[x]
variance.append(var)
varMin = min(varMin, var)
varMax = max(varMax, var)
norm = Normalize(vmin=varMin, vmax=varMax)
sm = ScalarMappable(norm, self.colourMap)
colours = sm.to_rgba(variance)
pc = PolyCollection(polys)
pc.set_gid('plot')
pc.set_norm(norm)
pc.set_color(colours)
self.axes.add_collection(pc)
return None, None
def pyplot_bar(y, cmap='Blues'): """ Make a good looking pylot bar plot. Use a colormap to color the bars. y: height of bars cmap: colormap, defaults to 'Blues' """ import matplotlib.pyplot as plt from matplotlib.colors import Normalize from matplotlib.cm import ScalarMappable vmax = numpy.max(y) vmin = (numpy.min(y)*3. - vmax)/2. colormap = ScalarMappable(norm=Normalize(vmin, vmax), cmap='Blues') plt.bar(numpy.arange(len(y)), y, color=colormap.to_rgba(y), align='edge', width=1.0)
def plotThomsonEdgeData(self, ax):
proNode = self.elecTree.getNode('\ELECTRONS::TOP.YAG_EDGETS.RESULTS:NE')
rhoNode = self.elecTree.getNode('\ELECTRONS::TOP.YAG_EDGETS.RESULTS:RMID')
rpro = proNode.data()
rrho = rhoNode.data()
rtime = proNode.dim_of().data()
goodTimes = rrho[0] > 0
pro = rpro[:,goodTimes]
rho = rrho[:,goodTimes]
time = rtime[goodTimes]
sm = ScalarMappable()
rhoColor = sm.to_rgba(-rho)
for i in range(rpro.shape[0]):
ax.plot(time, pro[i], c=np.mean(rhoColor[i],axis=0))
ax.set_ylabel('ne')
def multiTimeTrace(self, i, time, rhoFlat, data, ylabel, reverse=True):
if reverse:
sm = ScalarMappable(cmap='gist_rainbow')
else:
sm = ScalarMappable(cmap='gist_rainbow_r')
rhoColor = sm.to_rgba(rhoFlat)
for j in range(len(rhoFlat)):
if len(time.shape) > 1:
if (data.shape[0] < data.shape[1]):
self.axes[i].plot(time[j,:], data[j,:], c=rhoColor[j], linestyle='-', marker='.')
else:
self.axes[i].plot(time[:,j], data[:,j], c=rhoColor[j], linestyle='-', marker='.')
else:
if (data.shape[0] < data.shape[1]):
self.axes[i].plot(time, data[j,:], c=rhoColor[j], linestyle='-', marker='.')
else:
self.axes[i].plot(time, data[:,j], c=rhoColor[j], linestyle='-', marker='.')
self.axes[i].set_ylabel(ylabel)
def plot_res(self, ax, do_label=True, tag_leafs=False, zlim=False,
cmap='jet_r'):
from matplotlib.pyplot import get_cmap
from matplotlib import patheffects
from matplotlib.colors import LogNorm
from matplotlib.cm import ScalarMappable
# Create a color map using either zlim as given or max/min resolution.
cNorm = LogNorm(vmin=self.dx_min, vmax=self.dx_max, clip=True)
cMap = ScalarMappable(cmap=get_cmap(cmap), norm=cNorm)
dx_vals = {}
for key in self.tree:
if self[key].isLeaf:
color = cMap.to_rgba(self[key].dx)
dx_vals[self[key].dx] = 1.0
#if self[key].dx in res_colors:
# dx_vals[self[key].dx] = 1.0
# color=#res_colors[self[key].dx]
#else:
# color='k'
self[key].plot_res(ax, fc=color, label=key*tag_leafs)
if do_label:
ax.annotate('Resolution:', [1.02,0.99], xycoords='axes fraction',
color='k',size='medium')
for i,key in enumerate(sorted(dx_vals.keys())):
#dx_int = log2(key)
if key<1:
label = '1/%i' % (key**-1)
else:
label = '%i' % key
ax.annotate('%s $R_{E}$'%label, [1.02,0.87-i*0.1],
xycoords='axes fraction', color=cMap.to_rgba(key),
size='x-large',path_effects=[patheffects.withStroke(
linewidth=1,foreground='k')])
class ColorMapper(object):
def __init__(self, bottom_val, top_val, color_palette_name):
cnorm = mpl_Normalize(vmin=bottom_val, vmax=top_val)
comap = get_cmap(color_palette_name)
self.scalar_map = ScalarMappable(norm=cnorm, cmap=comap)
@staticmethod
def rgb_to_hex(rgb):
res = tuple([int(c * 255) for c in rgb])
return '#%02x%02x%02x' % res
def color_from_val(self, val):
return self.rgb_to_hex(self.scalar_map.to_rgba(val)[:3])
def _custom_colorbar(cmap, ncolors, labels, **kwargs):
"""Create a custom, discretized colorbar with correctly formatted/aligned labels.
It was inspired mostly by the example provided in http://beneathdata.com/how-to/visualizing-my-location-history/
:param cmap: the matplotlib colormap object you plan on using for your graph
:param ncolors: (int) the number of discrete colors available
:param labels: the list of labels for the colorbar. Should be the same length as ncolors.
:return: custom colorbar
"""
if ncolors <> len(labels):
raise MapperError("Number of colors is not compatible with the number of labels")
else:
norm = BoundaryNorm(range(0, ncolors), cmap.N)
mappable = ScalarMappable(cmap=cmap)
mappable.set_array([])
mappable.set_clim(-0.5, ncolors+0.5)
colorbar = plt.colorbar(mappable, **kwargs)
colorbar.set_ticks(np.linspace(0, ncolors, ncolors+1)+0.5)
colorbar.set_ticklabels(range(0, ncolors))
colorbar.set_ticklabels(labels)
return colorbar
def plot2d(x: Union[core.NeuronObject, core.Volume, np.ndarray,
List[Union[core.NeuronObject, np.ndarray, core.Volume]]],
method: Union[Literal['2d'], Literal['3d'],
Literal['3d_complex']] = '3d',
**kwargs) -> Tuple[mpl.figure.Figure, mpl.axes.Axes]:
"""Generate 2D plots of neurons and neuropils.
The main advantage of this is that you can save plot as vector graphics.
Important
---------
This function uses matplotlib which "fakes" 3D as it has only very limited
control over layering objects in 3D. Therefore neurites are not necessarily
plotted in the right Z order. This becomes especially troublesome when
plotting a complex scene with lots of neurons criss-crossing. See the
``method`` parameter for details. All methods use orthogonal projection.
Parameters
----------
x : TreeNeuron | MeshNeuron | NeuronList | Volume | Dotprops | np.ndarray
Objects to plot:
- multiple objects can be passed as list (see examples)
- numpy array of shape (n,3) is intepreted as points for
scatter plots
method : '2d' | '3d' (default) | '3d_complex'
Method used to generate plot. Comes in three flavours:
1. '2d' uses normal matplotlib. Neurons are plotted on
top of one another in the order their are passed to
the function. Use the ``view`` parameter (below) to
set the view (default = xy).
2. '3d' uses matplotlib's 3D axis. Here, matplotlib
decide the depth order (zorder) of plotting. Can
change perspective either interacively or by code
(see examples).
3. '3d_complex' same as 3d but each neuron segment is
added individually. This allows for more complex
zorders to be rendered correctly. Slows down
rendering though.
soma : bool, default=True
Plot soma if one exists. Size of the soma is determined
by the neuron's ``.soma_radius`` property which defaults
to the "radius" column for ``TreeNeurons``.
connectors : bool, default=True
Plot connectors.
connectors_only : boolean, default=False
Plot only connectors, not the neuron.
cn_size : int | float, default = 1
Size of connectors.
linewidth : int | float, default=.5
Width of neurites. Also accepts alias ``lw``.
linestyle : str, default='-'
Line style of neurites. Also accepts alias ``ls``.
autoscale : bool, default=True
If True, will scale the axes to fit the data.
scalebar : int | float | str | pint.Quantity, default=False
Adds scale bar. Provide integer, float or str to set
size of scalebar. Int|float are assumed to be in same
units as data. You can specify units in as string:
e.g. "1 um". For methods '3d' and '3d_complex', this
will create an axis object.
ax : matplotlib ax, default=None
Pass an ax object if you want to plot on an existing
canvas. Must match ``method`` - i.e. 2D or 3D axis.
figsize : tuple, default=(8, 8)
Size of figure.
color : None | str | tuple | list | dict, default=None
Use single str (e.g. ``'red'``) or ``(r, g, b)`` tuple
to give all neurons the same color. Use ``list`` of
colors to assign colors: ``['red', (1, 0, 1), ...].
Use ``dict`` to map colors to neuron IDs:
``{id: (r, g, b), ...}``.
palette : str | array | list of arrays, default=None
Name of a matplotlib or seaborn palette. If ``color`` is
not specified will pick colors from this palette.
color_by : str | array | list of arrays, default = None
Can be the name of a column in the node table of
``TreeNeurons`` or an array of (numerical or
categorical) values for each node. Numerical values will
be normalized. You can control the normalization by
passing a ``vmin`` and/or ``vmax`` parameter.
shade_by : str | array | list of arrays, default=None
Similar to ``color_by`` but will affect only the alpha
channel of the color. If ``shade_by='strahler'`` will
compute Strahler order if not already part of the node
table (TreeNeurons only). Numerical values will be
normalized. You can control the normalization by passing
a ``smin`` and/or ``smax`` parameter.
alpha : float [0-1], default=.9
Alpha value for neurons. Overriden if alpha is provided
as fourth value in ``color`` (rgb*a*). You can override
alpha value for connectors by using ``cn_alpha``.
clusters : list, default=None
A list assigning a cluster to each neuron (e.g.
``[0, 0, 0, 1, 1]``). Overrides ``color`` and uses
``palette`` to generate colors according to clusters.
depth_coloring : bool, default=False
If True, will color encode depth (Z). Overrides
``color``. Does not work with ``method = '3d_complex'``.
depth_scale : bool, default=True
If True and ``depth_coloring=True`` will plot a scale.
cn_mesh_colors : bool, default=False
If True, will use the neuron's color for its connectors.
group_neurons : bool, default=False
If True, neurons will be grouped. Works with SVG export
(not PDF). Does NOT work with ``method='3d_complex'``.
scatter_kws : dict, default={}
Parameters to be used when plotting points. Accepted
keywords are: ``size`` and ``color``.
view : tuple, default = ("x", "y")
Sets view for ``method='2d'``.
orthogonal : bool, default=True
Whether to use orthogonal or perspective view for
methods '3d' and '3d_complex'.
volume_outlines : bool | "both", default=True
If True will plot volume outline with no fill. Only
works with `method="2d"`.
dps_scale_vec : float
Scale vector for dotprops.
rasterize : bool, default=False
Neurons produce rather complex vector graphics which can
lead to large files when saving to SVG, PDF or PS. Use
this parameter to rasterize neurons and meshes/volumes
(but not axes or labels) to reduce file size.
Examples
--------
.. plot::
:context: close-figs
>>> import navis
>>> import matplotlib.pyplot as plt
Plot list of neurons as simple 2d
>>> nl = navis.example_neurons()
>>> fig, ax = navis.plot2d(nl, method='2d', view=('x', '-y'))
>>> plt.show() # doctest: +SKIP
Add a volume
.. plot::
:context: close-figs
>>> vol = navis.example_volume('LH')
>>> fig, ax = navis.plot2d([nl, vol], method='2d', view=('x', '-y'))
>>> plt.show() # doctest: +SKIP
Change neuron colors
.. plot::
:context: close-figs
>>> fig, ax = navis.plot2d(nl,
... method='2d',
... view=('x', '-y'),
... color=['r', 'g', 'b', 'm', 'c', 'y'])
>>> plt.show() # doctest: +SKIP
Plot in "fake" 3D
.. plot::
:context: close-figs
>>> fig, ax = navis.plot2d(nl, method='3d')
>>> plt.show() # doctest: +SKIP
>>> # In an interactive window you can dragging the plot to rotate
Plot in "fake" 3D and change perspective
.. plot::
:context: close-figs
>>> fig, ax = navis.plot2d(nl, method='3d')
>>> # Change view to frontal (for example neurons)
>>> ax.azim = ax.elev = 90
>>> # Change view to lateral
>>> ax.azim, ax.elev = 180, 180
>>> ax.elev = 0
>>> # Change view to top
>>> ax.azim, ax.elev = 90, 180
>>> # Tilted top view
>>> ax.azim, ax.elev = -130, -150
>>> # Move camera
>>> ax.dist = 6
>>> plt.show() # doctest: +SKIP
Plot using depth-coloring
.. plot::
:context: close-figs
>>> fig, ax = navis.plot2d(nl, method='3d', depth_coloring=True)
>>> plt.show() # doctest: +SKIP
To close all figures
>>> plt.close('all')
See the :ref:`plotting tutorial <plot_intro>` for more examples.
Returns
-------
fig, ax : matplotlib figure and axis object
See Also
--------
:func:`navis.plot3d`
Use this if you want interactive, perspectively correct renders
and if you don't need vector graphics as outputs.
:func:`navis.plot1d`
A nifty way to visualise neurons in a single dimension.
:func:`navis.plot_flat`
Plot neurons as flat structures (e.g. dendrograms).
"""
# Filter kwargs
_ACCEPTED_KWARGS = [
'soma', 'connectors', 'connectors_only', 'ax', 'color', 'colors', 'c',
'view', 'scalebar', 'cn_mesh_colors', 'linewidth', 'cn_size',
'cn_alpha', 'orthogonal', 'group_neurons', 'scatter_kws', 'figsize',
'linestyle', 'rasterize', 'clusters', 'synapse_layout', 'alpha',
'depth_coloring', 'autoscale', 'depth_scale', 'ls', 'lw',
'volume_outlines', 'radius', 'dps_scale_vec', 'palette', 'color_by',
'shade_by', 'vmin', 'vmax', 'smin', 'smax', 'norm_global'
]
wrong_kwargs = [a for a in kwargs if a not in _ACCEPTED_KWARGS]
if wrong_kwargs:
raise KeyError(f'Unknown kwarg(s): {", ".join(wrong_kwargs)}. '
f'Currently accepted: {", ".join(_ACCEPTED_KWARGS)}')
_METHOD_OPTIONS = ['2d', '3d', '3d_complex']
if method not in _METHOD_OPTIONS:
raise ValueError(f'Unknown method "{method}". Please use either: '
f'{",".join(_METHOD_OPTIONS)}')
connectors = kwargs.get('connectors', False)
connectors_only = kwargs.get('connectors_only', False)
ax = kwargs.pop('ax', None)
scalebar = kwargs.get('scalebar', None)
# Depth coloring
depth_coloring = kwargs.get('depth_coloring', False)
depth_scale = kwargs.get('depth_scale', True)
scatter_kws = kwargs.get('scatter_kws', {})
autoscale = kwargs.get('autoscale', True)
# Parse objects
(neurons, volumes, points, _) = utils.parse_objects(x)
# Generate colors
colors = kwargs.pop('color', kwargs.pop('c', kwargs.pop('colors', None)))
palette = kwargs.get('palette', None)
color_by = kwargs.get('color_by', None)
shade_by = kwargs.get('shade_by', None)
# Generate the colormaps
(neuron_cmap,
volumes_cmap) = prepare_colormap(colors,
neurons=neurons,
volumes=volumes,
palette=palette,
clusters=kwargs.get('clusters', None),
alpha=kwargs.get('alpha', None),
color_range=1)
if not isinstance(color_by, type(None)):
if not palette:
raise ValueError(
'Must provide `palette` (e.g. "viridis") argument '
'if using `color_by`')
neuron_cmap = vertex_colors(neurons,
by=color_by,
use_alpha=False,
palette=palette,
norm_global=kwargs.get(
'norm_global', True),
vmin=kwargs.get('vmin', None),
vmax=kwargs.get('vmax', None),
na=kwargs.get('na', 'raise'),
color_range=1)
if not isinstance(shade_by, type(None)):
alphamap = vertex_colors(
neurons,
by=shade_by,
use_alpha=True,
palette='viridis', # palette is irrelevant here
norm_global=kwargs.get('norm_global', True),
vmin=kwargs.get('smin', None),
vmax=kwargs.get('smax', None),
na=kwargs.get('na', 'raise'),
color_range=1)
new_colormap = []
for c, a in zip(neuron_cmap, alphamap):
if not (isinstance(c, np.ndarray) and c.ndim == 2):
c = np.tile(c, (a.shape[0], 1))
if c.shape[1] == 4:
c[:, 3] = a[:, 3]
else:
c = np.insert(c, 3, a[:, 3], axis=1)
new_colormap.append(c)
neuron_cmap = new_colormap
# Set axis projection
if method in ['3d', '3d_complex']:
if kwargs.get('orthogonal', True):
proj3d.persp_transformation = _orthogonal_proj
else:
proj3d.persp_transformation = _perspective_proj
# Generate axes
if not ax:
if method == '2d':
fig, ax = plt.subplots(figsize=kwargs.get('figsize', (8, 8)))
ax.set_aspect('equal')
elif method in ['3d', '3d_complex']:
fig = plt.figure(figsize=kwargs.get('figsize',
plt.figaspect(1) * 1.5))
ax = fig.add_subplot(111, projection='3d')
# This sets front view
ax.azim = -90
ax.elev = 0
ax.dist = 7
# Disallowed for 3D in matplotlib 3.1.0
# ax.set_aspect('equal')
# Make background transparent (nicer for dark themes)
fig.patch.set_alpha(0)
ax.patch.set_alpha(0)
# Check if correct axis were provided
else:
if not isinstance(ax, mpl.axes.Axes):
raise TypeError('Ax must be of type "mpl.axes.Axes", '
f'not "{type(ax)}"')
fig = ax.get_figure()
if method in ['3d', '3d_complex']:
if ax.name != '3d':
raise TypeError('Axis must be 3d.')
elif method == '2d':
if ax.name == '3d':
raise TypeError('Axis must be 2d.')
ax.had_data = ax.has_data()
# Prepare some stuff for depth coloring
if depth_coloring and not neurons.empty:
if method == '3d_complex':
raise Exception(
f'Depth coloring unavailable for method "{method}"')
elif method == '2d':
bbox = neurons.bbox
# Add to kwargs
xy = [
v.replace('-', '').replace('+', '')
for v in kwargs.get('view', ('x', 'y'))
]
z_ix = [
v[1] for v in [('x', 0), ('y', 1), ('z', 2)] if v[0] not in xy
]
kwargs['norm'] = plt.Normalize(vmin=bbox[z_ix, 0],
vmax=bbox[z_ix, 1])
# Plot volumes first
if volumes:
for i, v in enumerate(volumes):
_ = _plot_volume(v, volumes_cmap[i], method, ax, **kwargs)
# Create lines from segments
visuals = {}
for i, neuron in enumerate(
config.tqdm(neurons,
desc='Plot neurons',
leave=False,
disable=config.pbar_hide | len(neurons) < 2)):
if not connectors_only:
if isinstance(neuron, core.TreeNeuron) and kwargs.get(
'radius', False):
_neuron = conversion.tree2meshneuron(neuron)
_neuron.connectors = neuron.connectors
neuron = _neuron
if isinstance(neuron, core.TreeNeuron) and neuron.nodes.empty:
logger.warning(f'Skipping TreeNeuron w/o nodes: {neuron.id}')
elif isinstance(neuron,
core.MeshNeuron) and neuron.faces.size == 0:
logger.warning(f'Skipping MeshNeuron w/o faces: {neuron.id}')
elif isinstance(neuron, core.Dotprops) and neuron.points.size == 0:
logger.warning(f'Skipping Dotprops w/o points: {neuron.id}')
elif isinstance(neuron, core.TreeNeuron):
lc, sc = _plot_skeleton(neuron, neuron_cmap[i], method, ax,
**kwargs)
# Keep track of visuals related to this neuron
visuals[neuron] = {'skeleton': lc, 'somata': sc}
elif isinstance(neuron, core.MeshNeuron):
m = _plot_mesh(neuron, neuron_cmap[i], method, ax, **kwargs)
visuals[neuron] = {'mesh': m}
elif isinstance(neuron, core.Dotprops):
dp = _plot_dotprops(neuron, neuron_cmap[i], method, ax,
**kwargs)
visuals[neuron] = {'dotprop': dp}
elif isinstance(neuron, core.VoxelNeuron):
dp = _plot_voxels(neuron, neuron_cmap[i], method, ax, kwargs,
**scatter_kws)
visuals[neuron] = {'dotprop': dp}
else:
raise TypeError(
f"Don't know how to plot neuron of type '{type(neuron)}' ")
if (connectors or connectors_only) and neuron.has_connectors:
_ = _plot_connectors(neuron, neuron_cmap[i], method, ax, **kwargs)
for p in points:
_ = _plot_scatter(p, method, ax, kwargs, **scatter_kws)
if autoscale:
if method == '2d':
ax.autoscale(tight=True)
elif method in ['3d', '3d_complex']:
# Make sure data lims are set correctly
update_axes3d_bounds(ax)
# Rezie to have equal aspect
set_axes3d_equal(ax)
if scalebar is not None:
_ = _add_scalebar(scalebar, neurons, method, ax)
def set_depth():
"""Set depth information for neurons according to camera position."""
# Get projected coordinates
proj_co = mpl_toolkits.mplot3d.proj3d.proj_points(
all_co, ax.get_proj())
# Get min and max of z coordinates
z_min, z_max = min(proj_co[:, 2]), max(proj_co[:, 2])
# Generate a new normaliser
norm = plt.Normalize(vmin=z_min, vmax=z_max)
# Go over all neurons and update Z information
for neuron in visuals:
# Get this neurons colletion and coordinates
if 'skeleton' in visuals[neuron]:
c = visuals[neuron]['skeleton']
this_co = c._segments3d[:, 0, :]
elif 'mesh' in visuals[neuron]:
c = visuals[neuron]['mesh']
# Note that we only get every third position -> that's because
# these vectors actually represent faces, i.e. each vertex
this_co = c._vec.T[::3, [0, 1, 2]]
else:
raise ValueError(
f'Neither mesh nor skeleton found for neuron {neuron.id}')
# Get projected coordinates
this_proj = mpl_toolkits.mplot3d.proj3d.proj_points(
this_co, ax.get_proj())
# Normalise z coordinates
ns = norm(this_proj[:, 2]).data
# Set array
c.set_array(ns)
# No need for normaliser - already happened
c.set_norm(None)
if (isinstance(neuron, core.TreeNeuron) and
not isinstance(getattr(neuron, 'soma', None), type(None))):
# Get depth of soma(s)
soma = utils.make_iterable(neuron.soma)
soma_co = neuron.nodes.set_index('node_id').loc[soma][[
'x', 'y', 'z'
]].values
soma_proj = mpl_toolkits.mplot3d.proj3d.proj_points(
soma_co, ax.get_proj())
soma_cs = norm(soma_proj[:, 2]).data
# Set soma color
for cs, s in zip(soma_cs, visuals[neuron]['somata']):
s.set_color(cmap(cs))
def Update(event):
set_depth()
if depth_coloring:
cmap = mpl.cm.jet
if method == '2d' and depth_scale:
sm = ScalarMappable(norm=kwargs['norm'], cmap=cmap)
fig.colorbar(sm, ax=ax, fraction=.075, shrink=.5, label='Depth')
elif method == '3d':
# Collect all coordinates
all_co = []
for n in visuals:
if 'skeleton' in visuals[n]:
all_co.append(visuals[n]['skeleton']._segments3d[:, 0, :])
if 'mesh' in visuals[n]:
all_co.append(visuals[n]['mesh']._vec.T[:, [0, 1, 2]])
all_co = np.concatenate(all_co, axis=0)
fig.canvas.mpl_connect('draw_event', Update)
set_depth()
plt.axis('off')
return fig, ax
def initialize_chart(self):
x_attr = self.vis.get_attr_by_channel("x")[0]
y_attr = self.vis.get_attr_by_channel("y")[0]
x_attr_abv = x_attr.attribute
y_attr_abv = y_attr.attribute
if len(x_attr.attribute) > 25:
x_attr_abv = x_attr.attribute[:15] + "..." + x_attr.attribute[-10:]
if len(y_attr.attribute) > 25:
y_attr_abv = y_attr.attribute[:15] + "..." + y_attr.attribute[-10:]
df = self.data.dropna()
x_pts = df[x_attr.attribute]
y_pts = df[y_attr.attribute]
set_fig_code = ""
plot_code = ""
color_attr = self.vis.get_attr_by_channel("color")
if len(color_attr) == 1:
color_attr_name = color_attr[0].attribute
color_attr_type = color_attr[0].data_type
colors = df[color_attr_name].values
plot_code += f"colors = df['{color_attr_name}'].values\n"
unique = list(set(colors))
vals = [unique.index(i) for i in colors]
if color_attr_type == "quantitative":
self.fig, self.ax = matplotlib_setup(7, 5)
set_fig_code = "fig, ax = plt.subplots(figsize=(7, 5))\n"
self.ax.scatter(x_pts, y_pts, c=vals, cmap="Blues", alpha=0.5)
plot_code += f"ax.scatter(x_pts, y_pts, c={vals}, cmap='Blues', alpha=0.5)\n"
my_cmap = plt.cm.get_cmap("Blues")
max_color = max(colors)
sm = ScalarMappable(cmap=my_cmap, norm=plt.Normalize(0, max_color))
sm.set_array([])
cbar = plt.colorbar(sm, label=color_attr_name)
cbar.outline.set_linewidth(0)
plot_code += f"my_cmap = plt.cm.get_cmap('Blues')\n"
plot_code += f"""sm = ScalarMappable(
cmap=my_cmap,
norm=plt.Normalize(0, {max_color}))\n"""
plot_code += f"cbar = plt.colorbar(sm, label='{color_attr_name}')\n"
plot_code += f"cbar.outline.set_linewidth(0)\n"
else:
if len(unique) >= 16:
unique = unique[:16]
maxlen = 0
for i in range(len(unique)):
unique[i] = str(unique[i])
if len(unique[i]) > 26:
unique[i] = unique[i][:26] + "..."
if len(unique[i]) > maxlen:
maxlen = len(unique[i])
if maxlen > 20:
self.fig, self.ax = matplotlib_setup(9, 5)
set_fig_code = "fig, ax = plt.subplots(figsize=(9, 5))\n"
else:
self.fig, self.ax = matplotlib_setup(7, 5)
set_fig_code = "fig, ax = plt.subplots(figsize=(7, 5))\n"
cmap = "Set1"
if len(unique) > 9:
cmap = "tab20c"
scatter = self.ax.scatter(x_pts, y_pts, c=vals, cmap=cmap)
plot_code += f"scatter = ax.scatter(x_pts, y_pts, c={vals}, cmap={cmap})\n"
leg = self.ax.legend(
handles=scatter.legend_elements(num=range(0, len(unique)))[0],
labels=unique,
title=color_attr_name,
markerscale=2.0,
bbox_to_anchor=(1.05, 1),
loc="upper left",
ncol=1,
frameon=False,
fontsize="13",
)
scatter.set_alpha(0.5)
plot_code += f"""ax.legend(
handles=scatter.legend_elements(num=range(0, len({unique})))[0],
labels={unique},
title='{color_attr_name}',
markerscale=2.,
bbox_to_anchor=(1.05, 1),
loc='upper left',
ncol=1,
frameon=False,
fontsize='13')\n"""
plot_code += "scatter.set_alpha(0.5)\n"
else:
set_fig_code = "fig, ax = plt.subplots(figsize=(4.5, 4))\n"
self.ax.scatter(x_pts, y_pts, alpha=0.5)
plot_code += f"ax.scatter(x_pts, y_pts, alpha=0.5)\n"
self.ax.set_xlabel(x_attr_abv, fontsize="15")
self.ax.set_ylabel(y_attr_abv, fontsize="15")
self.code += "import numpy as np\n"
self.code += "from math import nan\n"
self.code += "from matplotlib.cm import ScalarMappable\n"
self.code += set_fig_code
self.code += f"x_pts = df['{x_attr.attribute}']\n"
self.code += f"y_pts = df['{y_attr.attribute}']\n"
self.code += plot_code
self.code += f"ax.set_xlabel('{x_attr_abv}', fontsize='15')\n"
self.code += f"ax.set_ylabel('{y_attr_abv}', fontsize='15')\n"
def plot_basemap(lons, lats, size, color, labels=None,
projection='cyl', resolution='l', continent_fill_color='0.8',
water_fill_color='1.0', colormap=None, colorbar=None,
marker="o", title=None, colorbar_ticklabel_format=None,
show=True, **kwargs): # @UnusedVariable
"""
Creates a basemap plot with a data point scatter plot.
:type lons: list/tuple of floats
:param lons: Longitudes of the data points.
:type lats: list/tuple of floats
:param lats: Latitudes of the data points.
:type size: float or list/tuple of floats
:param size: Size of the individual points in the scatter plot.
:type color: float or list/tuple of
floats/:class:`~obspy.core.utcdatetime.UTCDateTime`
:param color: Color information of the individual data points. Can be
:type labels: list/tuple of str
:param labels: Annotations for the individual data points.
:type projection: str, optional
:param projection: The map projection. Currently supported are
* ``"cyl"`` (Will plot the whole world.)
* ``"ortho"`` (Will center around the mean lat/long.)
* ``"local"`` (Will plot around local events)
Defaults to "cyl"
:type resolution: str, optional
:param resolution: Resolution of the boundary database to use. Will be
based directly to the basemap module. Possible values are
* ``"c"`` (crude)
* ``"l"`` (low)
* ``"i"`` (intermediate)
* ``"h"`` (high)
* ``"f"`` (full)
Defaults to ``"l"``
:type continent_fill_color: Valid matplotlib color, optional
:param continent_fill_color: Color of the continents. Defaults to
``"0.9"`` which is a light gray.
:type water_fill_color: Valid matplotlib color, optional
:param water_fill_color: Color of all water bodies.
Defaults to ``"white"``.
:type colormap: str, any matplotlib colormap, optional
:param colormap: The colormap for color-coding the events.
The event with the smallest property will have the
color of one end of the colormap and the event with the biggest
property the color of the other end with all other events in
between.
Defaults to None which will use the default colormap for the date
encoding and a colormap going from green over yellow to red for the
depth encoding.
:type colorbar: bool, optional
:param colorbar: When left `None`, a colorbar is plotted if more than one
object is plotted. Using `True`/`False` the colorbar can be forced
on/off.
:type title: str
:param title: Title above plot.
:type colorbar_ticklabel_format: str or function or
subclass of :class:`matplotlib.ticker.Formatter`
:param colorbar_ticklabel_format: Format string or Formatter used to format
colorbar tick labels.
:type show: bool
:param show: Whether to show the figure after plotting or not. Can be used
to do further customization of the plot before showing it.
"""
min_color = min(color)
max_color = max(color)
if isinstance(color[0], (datetime.datetime, UTCDateTime)):
datetimeplot = True
color = [date2num(t) for t in color]
else:
datetimeplot = False
scal_map = ScalarMappable(norm=Normalize(min_color, max_color),
cmap=colormap)
scal_map.set_array(np.linspace(0, 1, 1))
fig = plt.figure()
# The colorbar should only be plotted if more then one event is
# present.
if colorbar is not None:
show_colorbar = colorbar
else:
if len(lons) > 1 and hasattr(color, "__len__") and \
not isinstance(color, (str, native_str)):
show_colorbar = True
else:
show_colorbar = False
if projection == "local":
ax_x0, ax_width = 0.10, 0.80
else:
ax_x0, ax_width = 0.05, 0.90
if show_colorbar:
map_ax = fig.add_axes([ax_x0, 0.13, ax_width, 0.77])
cm_ax = fig.add_axes([ax_x0, 0.05, ax_width, 0.05])
plt.sca(map_ax)
else:
ax_y0, ax_height = 0.05, 0.85
if projection == "local":
ax_y0 += 0.05
ax_height -= 0.05
map_ax = fig.add_axes([ax_x0, ax_y0, ax_width, ax_height])
if projection == 'cyl':
bmap = Basemap(resolution=resolution)
elif projection == 'ortho':
bmap = Basemap(projection='ortho', resolution=resolution,
area_thresh=1000.0, lat_0=np.mean(lats),
lon_0=np.mean(lons))
elif projection == 'local':
if min(lons) < -150 and max(lons) > 150:
max_lons = max(np.array(lons) % 360)
min_lons = min(np.array(lons) % 360)
else:
max_lons = max(lons)
min_lons = min(lons)
lat_0 = max(lats) / 2. + min(lats) / 2.
lon_0 = max_lons / 2. + min_lons / 2.
if lon_0 > 180:
lon_0 -= 360
deg2m_lat = 2 * np.pi * 6371 * 1000 / 360
deg2m_lon = deg2m_lat * np.cos(lat_0 / 180 * np.pi)
if len(lats) > 1:
height = (max(lats) - min(lats)) * deg2m_lat
width = (max_lons - min_lons) * deg2m_lon
margin = 0.2 * (width + height)
height += margin
width += margin
else:
height = 2.0 * deg2m_lat
width = 5.0 * deg2m_lon
# do intelligent aspect calculation for local projection
# adjust to figure dimensions
w, h = fig.get_size_inches()
aspect = w / h
if show_colorbar:
aspect *= 1.2
if width / height < aspect:
width = height * aspect
else:
height = width / aspect
bmap = Basemap(projection='aeqd', resolution=resolution,
area_thresh=1000.0, lat_0=lat_0, lon_0=lon_0,
width=width, height=height)
# not most elegant way to calculate some round lats/lons
def linspace2(val1, val2, N):
"""
returns around N 'nice' values between val1 and val2
"""
dval = val2 - val1
round_pos = int(round(-np.log10(1. * dval / N)))
# Fake negative rounding as not supported by future as of now.
if round_pos < 0:
factor = 10 ** (abs(round_pos))
delta = round(2. * dval / N / factor) * factor / 2
else:
delta = round(2. * dval / N, round_pos) / 2
new_val1 = np.ceil(val1 / delta) * delta
new_val2 = np.floor(val2 / delta) * delta
N = (new_val2 - new_val1) / delta + 1
return np.linspace(new_val1, new_val2, N)
N1 = int(np.ceil(height / max(width, height) * 8))
N2 = int(np.ceil(width / max(width, height) * 8))
bmap.drawparallels(linspace2(lat_0 - height / 2 / deg2m_lat,
lat_0 + height / 2 / deg2m_lat, N1),
labels=[0, 1, 1, 0])
if min(lons) < -150 and max(lons) > 150:
lon_0 %= 360
meridians = linspace2(lon_0 - width / 2 / deg2m_lon,
lon_0 + width / 2 / deg2m_lon, N2)
meridians[meridians > 180] -= 360
bmap.drawmeridians(meridians, labels=[1, 0, 0, 1])
else:
msg = "Projection '%s' not supported." % projection
raise ValueError(msg)
# draw coast lines, country boundaries, fill continents.
plt.gca().set_axis_bgcolor(water_fill_color)
bmap.drawcoastlines(color="0.4")
bmap.drawcountries(color="0.75")
bmap.fillcontinents(color=continent_fill_color,
lake_color=water_fill_color)
# draw the edge of the bmap projection region (the projection limb)
bmap.drawmapboundary(fill_color=water_fill_color)
# draw lat/lon grid lines every 30 degrees.
bmap.drawmeridians(np.arange(-180, 180, 30))
bmap.drawparallels(np.arange(-90, 90, 30))
# compute the native bmap projection coordinates for events.
x, y = bmap(lons, lats)
# plot labels
if labels:
if 100 > len(lons) > 1:
for name, xpt, ypt, _colorpt in zip(labels, x, y, color):
# Check if the point can actually be seen with the current bmap
# projection. The bmap object will set the coordinates to very
# large values if it cannot project a point.
if xpt > 1e25:
continue
plt.text(xpt, ypt, name, weight="heavy",
color="k", zorder=100, **path_effect_kwargs)
elif len(lons) == 1:
plt.text(x[0], y[0], labels[0], weight="heavy", color="k",
**path_effect_kwargs)
scatter = bmap.scatter(x, y, marker=marker, s=size, c=color,
zorder=10, cmap=colormap)
if title:
plt.suptitle(title)
# Only show the colorbar for more than one event.
if show_colorbar:
if colorbar_ticklabel_format is not None:
if isinstance(colorbar_ticklabel_format, (str, native_str)):
formatter = FormatStrFormatter(colorbar_ticklabel_format)
elif hasattr(colorbar_ticklabel_format, '__call__'):
formatter = FuncFormatter(colorbar_ticklabel_format)
elif isinstance(colorbar_ticklabel_format, Formatter):
formatter = colorbar_ticklabel_format
locator = MaxNLocator(5)
else:
if datetimeplot:
locator = AutoDateLocator()
formatter = AutoDateFormatter(locator)
formatter.scaled[1 / (24. * 60.)] = '%H:%M:%S'
else:
locator = None
formatter = None
cb = Colorbar(cm_ax, scatter, cmap=colormap,
orientation='horizontal',
ticks=locator,
format=formatter)
# format=formatter)
# ticks=mpl.ticker.MaxNLocator(4))
cb.update_ticks()
if show:
plt.show()
return fig
def plot_embedding_v2(X,
values,
classes=None,
method='tSNE',
cmap='tab20',
figsize=(8, 8),
markersize=15,
dpi=600,
marker=None,
return_emb=False,
save=False,
save_emb=False,
show_legend=False,
show_axis_label=True,
**legend_params):
if marker is not None:
X = np.concatenate([X, marker], axis=0)
N = X.shape[0]
if X.shape[1] != 2:
if method == 'tSNE':
from sklearn.manifold import TSNE
#X = TSNE(n_components=2, random_state=124,metric='correlation').fit_transform(X)
X = TSNE(n_components=2, random_state=124).fit_transform(X)
if method == 'PCA':
from sklearn.decomposition import PCA
X = PCA(n_components=2, random_state=124).fit_transform(X)
if method == 'UMAP':
from umap import UMAP
X = UMAP(n_neighbors=15, min_dist=0.1,
metric='correlation').fit_transform(X)
cmap = 'RdBu_r'
from matplotlib.cm import ScalarMappable
from matplotlib.colors import Normalize
sm = ScalarMappable(norm=Normalize(vmin=-np.max(values),
vmax=np.max(values)),
cmap='RdBu_r')
#colors = sns.color_palette('husl', n_colors=len(classes),desat=0.7)
#colors = sns.husl_palette(len(classes), s=.8)
for i in range(N):
plt.scatter(X[i, 0],
X[i, 1],
s=markersize,
color=sm.to_rgba(values[i]))
legend_params_ = {
'loc': 'center left',
'bbox_to_anchor': (1.0, 0.45),
'fontsize': 20,
'ncol': 1,
'frameon': False,
'markerscale': 1.5
}
legend_params_.update(**legend_params)
if show_legend:
plt.legend(**legend_params_)
sns.despine(offset=10, trim=True)
if show_axis_label:
plt.xlabel(method + ' dim 1', fontsize=12)
plt.ylabel(method + ' dim 2', fontsize=12)
if save:
plt.savefig(save, format='png', bbox_inches='tight', dpi=dpi)
def plot_depth(image_key, pixel_age, flag_map, # validity_map,
depth_map_true, depth_map_pred, variance_map,
image_cmap="gray", depth_cmap='RdBu'):
fig = plt.figure()
ax = fig.add_subplot(2, 4, 1)
ax.set_title("keyframe")
ax.imshow(image_key, cmap=image_cmap)
ax = fig.add_subplot(2, 4, 2)
ax.set_title("pixel age")
im = ax.imshow(pixel_age, cmap=image_cmap)
plot_with_bar(ax, im)
ax = fig.add_subplot(2, 4, 3)
ax.set_title("flag map")
ax.imshow(flag_to_color_map(flag_map))
patches = [Patch("black", flag_to_rgb(f), label=f.name) for f in FLAG]
ax.legend(handles=patches, loc='center left', bbox_to_anchor=(1.05, 0.5))
mask = flag_map==FLAG.SUCCESS
if mask.sum() == 0: # no success pixel
plt.show()
return
depths_pred = depth_map_pred[mask]
depths_true = depth_map_true[mask]
depths_diff = np.abs(depths_pred - depths_true)
us = image_coordinates(depth_map_pred.shape)[mask.flatten()]
vmax = np.percentile(
np.concatenate((depth_map_true.flatten(), depths_pred)), 98
)
norm = Normalize(vmin=0.0, vmax=vmax)
mapper = ScalarMappable(norm=norm, cmap=depth_cmap)
ax = fig.add_subplot(2, 4, 5)
ax.set_title("ground truth depth")
ax.axis("off")
im = ax.imshow(depth_map_true, norm=norm, cmap=depth_cmap)
plot_with_bar(ax, im)
height, width = image_key.shape[0:2]
ax = fig.add_subplot(2, 4, 6)
ax.set_title("predicted depth map")
ax.axis("off")
im = ax.imshow(image_key, cmap=image_cmap)
ax.scatter(us[:, 0], us[:, 1], s=0.5, c=mapper.to_rgba(depths_pred))
ax.set_xlim(0, width)
ax.set_ylim(height, 0)
ax = fig.add_subplot(2, 4, 7)
ax.set_title("error = abs(pred - true)")
im = ax.imshow(image_key, cmap=image_cmap)
ax.scatter(us[:, 0], us[:, 1], s=0.5, c=mapper.to_rgba(depths_diff))
ax.set_xlim(0, width)
ax.set_ylim(height, 0)
ax = fig.add_subplot(2, 4, 8)
ax.set_title("variance map")
norm = Normalize(vmin=0.0, vmax=np.percentile(variance_map.flatten(), 98))
im = ax.imshow(variance_map, norm=norm, cmap=depth_cmap)
plot_with_bar(ax, im)
plt.show()
def contam(dval):
"""
Function to plot the contamination against the stellar TESS magnitudes
.. codeauthor:: Mikkel N. Lund <*****@*****.**>
.. codeauthor:: Rasmus Handberg <*****@*****.**>
"""
logger = logging.getLogger('dataval')
logger.info('Plotting Contamination vs. Magnitude...')
xmax = np.arange(0, 21, 1)
ymax = np.array([
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.12, 0.2, 0.3, 0.45, 0.6, 0.7, 0.8, 0.9,
0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9
])
cont_vs_mag = InterpolatedUnivariateSpline(xmax, ymax, k=1, ext=3)
for cadence in dval.cadences:
fig, ax = plt.subplots() # plt.figaspect(2.0)
fig.subplots_adjust(left=0.145,
wspace=0.3,
top=0.945,
bottom=0.145,
right=0.975)
# Search database for all targets processed with aperture photometry:
star_vals = dval.search_database(
select=[
'todolist.priority', 'todolist.sector', 'todolist.tmag',
'contamination'
],
search=["method_used='aperture'", f'cadence={cadence:d}'])
rgba_color = 'k'
if dval.color_by_sector:
sec = np.array([star['sector'] for star in star_vals],
dtype='int32')
sectors = list(set(sec))
if len(sectors) > 1:
norm = colors.Normalize(vmin=1, vmax=len(sectors))
scalarMap = ScalarMappable(norm=norm,
cmap=plt.get_cmap('Set1'))
rgba_color = np.array([scalarMap.to_rgba(s) for s in sec])
pri = np.array([star['priority'] for star in star_vals], dtype='int64')
tmags = np.array([star['tmag'] for star in star_vals], dtype='float64')
contam = np.array([star['contamination'] for star in star_vals],
dtype='float64')
# Indices for plotting
idx_invalid = np.isnan(contam)
with np.errstate(
invalid='ignore'): # We know that some cont may be NaN
idx_high = (contam > 1)
idx_low = (contam <= 1)
# Remove nan contaminations (should be only from Halo targets)
contam[idx_high] = 1.1
contam[idx_invalid] = 1.2
# Plot individual contamination points
ax.scatter(tmags[idx_low],
contam[idx_low],
marker='o',
facecolors=rgba_color,
color=rgba_color,
alpha=0.1,
rasterized=True)
ax.scatter(tmags[idx_high],
contam[idx_high],
marker='o',
facecolors='None',
color=rgba_color,
alpha=0.9)
# Plot invalid points:
ax.scatter(tmags[idx_invalid],
contam[idx_invalid],
marker='o',
facecolors='None',
color='r',
alpha=0.9)
# Indices for finding validation limit
#idx_low = (contam <= 1)
# Compute median-bin curve
#bin_means, bin_edges, binnumber = binning(tmags[idx_low], contam[idx_low], statistic='median', bins=15, range=(np.nanmin(tmags),np.nanmax(tmags)))
#bin_width = (bin_edges[1] - bin_edges[0])
#bin_centers = bin_edges[1:] - bin_width/2
# Plot median-bin curve (1 and 5 times standadised MAD)
#ax1.scatter(bin_centers, 1.4826*bin_means, marker='o', color='r')
#ax1.scatter(bin_centers, 1.4826*5*bin_means, marker='.', color='r')
#ax.plot(xmax, ymax, marker='.', color='r', ls='-')
mags = np.linspace(dval.tmag_limits[0], dval.tmag_limits[1], 100)
ax.plot(mags, cont_vs_mag(mags), 'r-')
ax.set_xlim(dval.tmag_limits)
ax.set_ylim([-0.05, 1.3])
ax.axhline(y=0, ls='--', color='k', zorder=-1)
ax.axhline(y=1.1, ls=':', color='k', zorder=-1)
ax.axhline(y=1.2, ls=':', color='r', zorder=-1)
ax.axhline(y=1, ls=':', color='k', zorder=-1)
ax.set_xlabel('TESS magnitude')
ax.set_ylabel('Contamination')
ax.xaxis.set_major_locator(MultipleLocator(2))
ax.xaxis.set_minor_locator(MultipleLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(0.2))
ax.yaxis.set_minor_locator(MultipleLocator(0.1))
###########
fig.savefig(os.path.join(dval.outfolder, f'contam_c{cadence:04d}'))
if not dval.show:
plt.close(fig)
# Assign validation bits
dv = np.zeros_like(pri, dtype='int32')
dv[contam >= 1] |= DatavalQualityFlags.InvalidContamination
dv[(contam > cont_vs_mag(tmags))
& (contam < 1)] |= DatavalQualityFlags.ContaminationHigh
dval.update_dataval(pri, dv)
def draw(self):
# TODO: recycle the figure with `self.fig.clear()` rather than creating new panel and figure each refresh!
gs = mpl.gridspec.GridSpec(1, 3, width_ratios=[85, 5, 10], wspace=0.025)
# --- Main dot scatter plot ---
self.dot_plot = dot_plot = self.figure.add_subplot(gs[0])
miny, maxy = self.config['freqminmax']
plot_kwargs = dict(cmap=self.config['colormap'],
vmin=self.SLOPE_MIN, vmax=self.SLOPE_MAX, # vmin/vmax define where we scale our colormap
c=self.slopes, s=self.scaled_amplitudes, # dot color and size
linewidths=0.0,
)
def plot_harmonics(x):
"""Reusable way to plot harmonics from different view types"""
if self.config['harmonics']['0.5']:
dot_plot.scatter(x, self.freqs/2, alpha=0.2, **plot_kwargs)
if self.config['harmonics']['2']:
dot_plot.scatter(x, self.freqs*2, alpha=0.2, **plot_kwargs)
if self.config['harmonics']['3']:
dot_plot.scatter(x, self.freqs*3, alpha=0.2, **plot_kwargs)
if len(self.freqs) < 2:
dot_scatter = dot_plot.scatter([], []) # empty set
elif not self.config['compressed']:
# Realtime View
plot_harmonics(self.times)
dot_scatter = dot_plot.scatter(self.times, self.freqs, **plot_kwargs)
dot_plot.set_xlim(self.times[0], self.times[-1])
dot_plot.set_xlabel('Time (sec)')
else:
# Compressed (pseudo-Dot-Per-Pixel) View
if self.config['pulse_markers']:
for v in self.zc.get_pulses():
dot_plot.axvline(v, linewidth=0.5, color='#808080')
x = range(len(self.freqs))
plot_harmonics(x)
dot_scatter = dot_plot.scatter(x, self.freqs, **plot_kwargs)
dot_plot.set_xlim(0, len(x))
dot_plot.set_xlabel('Dot Count')
try:
dot_plot.set_yscale(self.config['scale']) # FIXME: fails with "Data has no positive values" error
except ValueError:
log.exception('Failed setting log scale (exception caught)')
log.error('\ntimes: %s\nfreqs: %s\nslopes: %s', self.times, self.freqs, self.slopes)
dot_plot.set_title(self.name)
dot_plot.set_ylabel('Frequency (kHz)')
dot_plot.set_ylim(miny, maxy)
# remove the default tick labels, then produce our own instead
dot_plot.yaxis.set_minor_formatter(mpl.ticker.NullFormatter())
dot_plot.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
minytick = miny if miny % 10 == 0 else miny + 10 - miny % 10 # round up to next 10kHz tick
maxytick = maxy if maxy % 10 == 0 else maxy + 10 - maxy % 10
ticks = range(minytick, maxytick+1, 10) # labels every 10kHz
dot_plot.yaxis.set_ticks(ticks)
dot_plot.set_axisbelow(True)
dot_plot.grid(axis='y', which='both', linestyle=':')
for freqk in self.config['markers']:
dot_plot.axhline(freqk, color='r', linewidth=1.0, zorder=0.9)
for freqk in self.config['filter_markers']:
dot_plot.axhline(freqk, color='b', linestyle='--', linewidth=1.1, zorder=0.95)
# draw X and Y cursor; this may beform better if we can use Wx rather than MatPlotLib, see `wxcursor_demo.py`
if self.config['display_cursor']:
self.cursor1 = Cursor(dot_plot, useblit=True, color='black', linewidth=1)
# experimental rectangle selection
def onselect(eclick, erelease):
"""eclick and erelease are matplotlib events at press and release"""
x1, y1 = eclick.xdata, eclick.ydata
x2, y2 = erelease.xdata, erelease.ydata
log.debug(' Select (%.3f,%.1f) -> (%.3f,%.1f) button: %d' % (x1, y1, x2, y2, eclick.button))
if self.config['compressed']:
x1, x2 = self.zc.times[int(round(x1))], self.zc.times[int(round(x2))]
slope = (np.log2(y2) - np.log2(y1)) / (x2 - x1)
log.debug(' slope: %.1f oct/sec (%.1f kHz / %.3f sec)' % (slope, y2 - y1, x2 - x1))
self.selector = RectangleSelector(dot_plot, onselect, drawtype='box')
#connect('key_press_event', toggle_selector)
# --- Colorbar plot ---
self.cbar_plot = cbar_plot = self.figure.add_subplot(gs[1])
cbar_plot.set_title('Slope')
try:
cbar = self.figure.colorbar(dot_scatter, cax=cbar_plot, ticks=[])
except TypeError:
# colorbar() blows up on empty set
sm = ScalarMappable(cmap=self.config['colormap']) # TODO: this should probably share colormap code with histogram
sm.set_array(np.array([self.SLOPE_MIN, self.SLOPE_MAX]))
cbar = self.figure.colorbar(sm, cax=cbar_plot, ticks=[])
cbar.ax.set_yticklabels([])
# --- Hist plot ---
self.hist_plot = hist_plot = self.figure.add_subplot(gs[2])
hist_plot.set_title('Freqs')
bin_min, bin_max = self.config['freqminmax']
bin_size = 2 # khz # TODO: make this configurable
bin_n = int((bin_max - bin_min) / bin_size)
n, bins, patches = hist_plot.hist(self.freqs, weights=self.amplitudes,
range=self.config['freqminmax'], bins=bin_n,
orientation='horizontal',
edgecolor='black')
hist_plot.set_yscale(self.config['scale'])
hist_plot.set_ylim(miny, maxy)
hist_plot.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
# color histogram bins
cmap = ScalarMappable(cmap=self.config['colormap'], norm=Normalize(vmin=self.SLOPE_MIN, vmax=self.SLOPE_MAX))
for bin_start, bin_end, patch in zip(bins[:-1], bins[1:], patches):
bin_mask = (bin_start <= self.freqs) & (self.freqs < bin_end)
bin_slopes = self.slopes[bin_mask]
slope_weights = self.scaled_amplitudes[bin_mask]
avg_slope = np.average(bin_slopes, weights=slope_weights) if bin_slopes.any() else 0.0
#avg_slope = np.median(bin_slopes) if bin_slopes.any() else 0
patch.set_facecolor(cmap.to_rgba(avg_slope))
hist_plot.yaxis.set_minor_formatter(mpl.ticker.NullFormatter())
hist_plot.yaxis.set_ticks(ticks)
hist_plot.yaxis.tick_right()
hist_plot.xaxis.set_ticks([])
hist_plot.set_axisbelow(True)
hist_plot.grid(axis='y', which='both', linestyle=':')
for freqk in self.config['markers']:
hist_plot.axhline(freqk, color='r', linewidth=1.0, zorder=0.9)
for freqk in self.config['filter_markers']:
hist_plot.axhline(freqk, color='b', linestyle='--', linewidth=1.1, zorder=0.95)
# draw Y cursor
if self.config['display_cursor']:
self.cursor3 = Cursor(hist_plot, useblit=True, color='black', linewidth=1, vertOn=False, horizOn=True)
data_color[i] = (threshold - means[i] + ci_95[i]) / (2 * ci_95[i])
if data_color[i] > 1: data_color[i] = 1
if data_color[i] < 0: data_color[i] = 0
my_cmap = plt.cm.get_cmap('RdBu')
plt.bar(x_ticks, means, width=0.8, yerr=ci_95, capsize=5, color=my_cmap(data_color))
###plot bars
updateColors(40000)
plt.xticks(x_ticks, years)
axline = plt.axhline(y=40000, clip_on=False, zorder=1, color='red')
txt = plt.gca().text(x=5.5, y=40000, s=40000, color='red')
plt.title('Confidence Interval Interactivity: \nClick the Chart To Recolor')
#show colorbar
my_cmap = plt.cm.get_cmap('RdBu')
sm = ScalarMappable(cmap=my_cmap)
sm.set_array([])
cbar = plt.colorbar(sm)
cbar.set_label('Color', rotation=270,labelpad=25)
###interactivity
def moveAxHLine(event):
axline.set_ydata(event.ydata)
#reset the interest data displayal
txt.set_position((5.5, event.ydata))
txt.set_text(event.ydata)
updateColors(event.ydata)
plt.gcf().canvas.mpl_connect('button_press_event', moveAxHLine)
cases_at_measurement_times = np.interp(days_elapsed + delta_t, times,
cumulative_incidence)
accept = (
(np.asarray(min_number_cases) < cases_at_measurement_times) &
(cases_at_measurement_times < np.asarray(max_number_cases))).all()
cax = ax.semilogy(times,
cumulative_incidence,
'.-',
color=cmap(R0_grid[j]),
alpha=1.0 if accept else 0.25,
zorder=1 if not accept else 5)
plot_kwargs = dict(fmt='s', color='k', zorder=10, ecolor='k')
ax.errorbar(48, 2890, xerr=14, yerr=[[2700], [2700]], **plot_kwargs)
ax.errorbar(52, 5000, xerr=14, yerr=[[4000], [4700]], **plot_kwargs)
norm = Normalize(vmin=R0_grid.min(), vmax=R0_grid.max())
cbar = plt.colorbar(mappable=ScalarMappable(norm=norm, cmap=plt.cm.viridis),
label='$\mathcal{R}_0$')
ax.set_xticks(np.arange(0, 60, 1), minor=True)
for sp in ['right', 'top']:
ax.spines[sp].set_visible(False)
ax.set_xlabel('Time [days]')
ax.set_ylabel('Cumulative Incidence')
fig.tight_layout()
fig.savefig('plots/trajectories.pdf', bbox_inches='tight')
plt.show()
def add_collage_colorbar(figure,
ax,
smfs,
vmin,
vmax,
cbar_vmin=None,
cbar_vmax=None,
midpoint=None,
multicollage=False,
colorbar_params={},
**kwargs):
if 'fraction' in colorbar_params:
fraction = colorbar_params['fraction']
else:
fraction = .5
if 'shrink' in colorbar_params:
shrink = colorbar_params['shrink']
else:
shrink = .5
if 'aspect' in colorbar_params:
aspect = colorbar_params['aspect']
else:
aspect = 20
if 'pad' in colorbar_params:
pad = colorbar_params['pad']
else:
pad = .5
if 'anchor' in colorbar_params:
anchor = colorbar_params['anchor']
else:
anchor = 'C'
if 'format' in colorbar_params:
c_format = colorbar_params['format']
else:
c_format = '%.2g'
if 'cmap' not in kwargs:
cmap = None
else:
cmap = kwargs.pop('cmap')
if cmap is None:
cmap = get_cmap(plt.rcParamsDefault['image.cmap'])
else:
if isinstance(cmap, str):
cmap = get_cmap(cmap)
if 'threshold' in kwargs:
threshold = kwargs['threshold']
else:
threshold = None
# Color bar
our_cmap = get_cmap(cmap)
if midpoint is None:
norm = Normalize(vmin=vmin, vmax=vmax)
else:
norm = MidpointNormalize(midpoint=midpoint, vmin=vmin, vmax=vmax)
nb_ticks = 5
ticks = np.linspace(vmin, vmax, nb_ticks)
bounds = np.linspace(vmin, vmax, our_cmap.N)
if threshold is not None:
cmaplist = [our_cmap(i) for i in range(our_cmap.N)]
# set colors to grey for absolute values < threshold
istart = int(norm(-threshold, clip=True) * (our_cmap.N - 1))
istop = int(norm(threshold, clip=True) * (our_cmap.N - 1))
for i in range(istart, istop):
cmaplist[i] = (0.5, 0.5, 0.5, 1.)
our_cmap = LinearSegmentedColormap.from_list('Custom cmap', cmaplist,
our_cmap.N)
# we need to create a proxy mappable
proxy_mappable = ScalarMappable(cmap=our_cmap, norm=norm)
proxy_mappable.set_array(np.concatenate(smfs))
if multicollage:
cbar = plt.colorbar(proxy_mappable,
ax=ax,
ticks=ticks,
spacing='proportional',
format=c_format,
orientation='vertical',
anchor=anchor,
fraction=fraction,
shrink=shrink,
aspect=aspect,
pad=pad)
else:
left = (ax[0][0].get_position().x0 + ax[0][0].get_position().x1) / 2
right = (ax[0][1].get_position().x0 + ax[0][1].get_position().x1) / 2
bot = ax[1][0].get_position().y1
width = right - left
# [left, bottom, width, height]
cbaxes = figure.add_axes([left, bot - (.05 / 3), width, .05])
cbar = plt.colorbar(proxy_mappable,
cax=cbaxes,
ticks=ticks,
spacing='proportional',
format=c_format,
orientation='horizontal',
shrink=1,
anchor='C')
_crop_colorbar(cbar, cbar_vmin, cbar_vmax)
return figure, ax
def animate_contour(xdata=None,
ydata=None,
zdata=None,
times=None,
timescale='s',
title='',
xlabel='',
ylabel='',
cmap=cm_hot_desaturated,
zlims=None,
levels=100,
save=False,
filename=None):
nt = len(zdata)
if xdata is None:
m = zdata[0].shape[1]
xdata = [i for i in range(m)]
if ydata is None:
n = zdata[0].shape[0]
ydata = [j for j in range(n)]
vmin = zlims[0] if zlims is not None else np.min(zdata)
vmax = zlims[1] if zlims is not None else np.max(zdata)
norm = Normalize(vmin=vmin, vmax=vmax)
if times is None:
title_str = title + f'\n0/{nt}'
else:
title_str = title + f'\n{times[0]:4.3f}{timescale}'
fig, ax = plt.subplots(1, 1)
fig_title = ax.set_title(title_str)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
cax = make_axes_locatable(ax).append_axes('right', '5%', '5%')
im = ax.contourf(xdata,
ydata,
zdata[0],
cmap=cmap,
levels=levels,
norm=norm)
if zlims is not None:
fig.colorbar(ScalarMappable(norm=norm, cmap=cmap), cax=cax)
def animate(i):
if zlims is None:
im = ax.contourf(xdata, ydata, zdata[i], cmap=cmap, levels=levels)
cax.clear()
fig.colorbar(im, cax=cax)
else:
im = ax.contourf(xdata,
ydata,
zdata[i],
cmap=cmap,
levels=levels,
norm=norm)
if times is None:
title_str = title + f'\n{i}/{nt}'
else:
title_str = title + f'\n{times[i]:4.3f}{timescale}'
fig_title.set_text(title_str)
return im,
anim = FuncAnimation(fig,
animate,
frames=len(zdata),
interval=100,
blit=False)
if save:
anim.save(filename, writer='ffmpeg')
print(f'Animation saved as {filename}.')
else:
plt.show()
yerr=(error_low, error_high),
error_kw={
'capsize': 10,
'elinewidth': 2,
'alpha': 1
}))
###Create and display textarea widget
txt = wdg.Textarea(value='',
placeholder='',
description='Y Value:',
disabled=False)
### Formats color bar. Need the scalar mapable to enable use of the color bar.
my_cmap = plt.cm.get_cmap('coolwarm')
sm = ScalarMappable(cmap=my_cmap, norm=plt.Normalize(0, 1))
sm.set_array([])
cbar = plt.colorbar(sm)
cbar.set_label('Probability', rotation=270, labelpad=25)
ydataselect = 40000
class ClickChart(object):
def __init__(self, ax):
self.fig = ax.figure
self.ax = ax
self.horiz_line = ax.axhline(y=ydataselect, color='black', linewidth=2)
self.fig.canvas.mpl_connect('button_press_event', self.onclick)
m.fillcontinents(color='#f2f2f2', lake_color='#46bcec')
m.drawcoastlines()
#contains all state position coordinates
m.readshapefile("INDIA/IND_adm1", "INDIA")
colors = {}
statenames = []
patches = []
#Colormap
cmap = plt.cm.Reds
#Colorbar Range
vmin = min(color_dict.values())
vmax = max(color_dict.values())
norm = Normalize(vmin=vmin, vmax=vmax)
# color mapper to covert values to colors
mapper = ScalarMappable(norm=norm, cmap=cmap)
for shapedict in m.INDIA_info:
statename = shapedict['NAME_1']
#To incorporate difference between Map State Name and Data Loc Name
if statename == "Telangana":
statename = "Telengana"
if statename in color_dict:
pop = color_dict[statename]
colors[statename] = mapper.to_rgba(pop)
statenames.append(statename)
for nshape, seg in enumerate(m.INDIA):
color = rgb2hex(colors[statenames[nshape]])
poly = Polygon(seg, facecolor=color, edgecolor=color)
ax.add_patch(poly)
def plot_surf(surf_mesh,
surf_map=None,
bg_map=None,
hemi='left',
view='lateral',
cmap=None,
colorbar=False,
avg_method='mean',
threshold=None,
alpha='auto',
bg_on_data=False,
darkness=1,
vmin=None,
vmax=None,
cbar_vmin=None,
cbar_vmax=None,
title=None,
output_file=None,
axes=None,
figure=None,
**kwargs):
""" Plotting of surfaces with optional background and data
.. versionadded:: 0.3
Parameters
----------
surf_mesh: str or list of two numpy.ndarray
Surface mesh geometry, can be a file (valid formats are
.gii or Freesurfer specific files such as .orig, .pial,
.sphere, .white, .inflated) or
a list of two Numpy arrays, the first containing the x-y-z coordinates
of the mesh vertices, the second containing the indices
(into coords) of the mesh faces.
surf_map: str or numpy.ndarray, optional.
Data to be displayed on the surface mesh. Can be a file (valid formats
are .gii, .mgz, .nii, .nii.gz, or Freesurfer specific files such as
.thickness, .curv, .sulc, .annot, .label) or
a Numpy array with a value for each vertex of the surf_mesh.
bg_map: Surface data object (to be defined), optional,
Background image to be plotted on the mesh underneath the
surf_data in greyscale, most likely a sulcal depth map for
realistic shading.
hemi : {'left', 'right'}, default is 'left'
Hemisphere to display.
view: {'lateral', 'medial', 'dorsal', 'ventral', 'anterior', 'posterior'},
default is 'lateral'
View of the surface that is rendered.
cmap: matplotlib colormap, str or colormap object, default is None
To use for plotting of the stat_map. Either a string
which is a name of a matplotlib colormap, or a matplotlib
colormap object. If None, matplotlib default will be chosen
colorbar : bool, optional, default is False
If True, a colorbar of surf_map is displayed.
avg_method: {'mean', 'median'}, default is 'mean'
How to average vertex values to derive the face value, mean results
in smooth, median in sharp boundaries.
threshold : a number or None, default is None.
If None is given, the image is not thresholded.
If a number is given, it is used to threshold the image, values
below the threshold (in absolute value) are plotted as transparent.
alpha: float, alpha level of the mesh (not surf_data), default 'auto'
If 'auto' is chosen, alpha will default to .5 when no bg_map
is passed and to 1 if a bg_map is passed.
bg_on_data: bool, default is False
If True, and a bg_map is specified, the surf_data data is multiplied
by the background image, so that e.g. sulcal depth is visible beneath
the surf_data.
NOTE: that this non-uniformly changes the surf_data values according
to e.g the sulcal depth.
darkness: float, between 0 and 1, default is 1
Specifying the darkness of the background image.
1 indicates that the original values of the background are used.
.5 indicates the background values are reduced by half before being
applied.
vmin, vmax: lower / upper bound to plot surf_data values
If None , the values will be set to min/max of the data
title : str, optional
Figure title.
output_file: str, or None, optional
The name of an image file to export plot to. Valid extensions
are .png, .pdf, .svg. If output_file is not None, the plot
is saved to a file, and the display is closed.
axes: instance of matplotlib axes, None, optional
The axes instance to plot to. The projection must be '3d' (e.g.,
`figure, axes = plt.subplots(subplot_kw={'projection': '3d'})`,
where axes should be passed.).
If None, a new axes is created.
figure: instance of matplotlib figure, None, optional
The figure instance to plot to. If None, a new figure is created.
See Also
--------
nilearn.datasets.fetch_surf_fsaverage : For surface data object to be
used as background map for this plotting function.
nilearn.plotting.plot_surf_roi : For plotting statistical maps on brain
surfaces.
nilearn.plotting.plot_surf_stat_map : for plotting statistical maps on
brain surfaces.
"""
# load mesh and derive axes limits
mesh = load_surf_mesh(surf_mesh)
coords, faces = mesh[0], mesh[1]
limits = [coords.min(), coords.max()]
# set view
if hemi == 'right':
if view == 'lateral':
elev, azim = 0, 0
elif view == 'medial':
elev, azim = 0, 180
elif view == 'dorsal':
elev, azim = 90, 0
elif view == 'ventral':
elev, azim = 270, 0
elif view == 'anterior':
elev, azim = 0, 90
elif view == 'posterior':
elev, azim = 0, 270
else:
raise ValueError('view must be one of lateral, medial, '
'dorsal, ventral, anterior, or posterior')
elif hemi == 'left':
if view == 'medial':
elev, azim = 0, 0
elif view == 'lateral':
elev, azim = 0, 180
elif view == 'dorsal':
elev, azim = 90, 0
elif view == 'ventral':
elev, azim = 270, 0
elif view == 'anterior':
elev, azim = 0, 90
elif view == 'posterior':
elev, azim = 0, 270
else:
raise ValueError('view must be one of lateral, medial, '
'dorsal, ventral, anterior, or posterior')
else:
raise ValueError('hemi must be one of right or left')
# set alpha if in auto mode
if alpha == 'auto':
if bg_map is None:
alpha = .5
else:
alpha = 1
# if no cmap is given, set to matplotlib default
if cmap is None:
cmap = plt.cm.get_cmap(plt.rcParamsDefault['image.cmap'])
else:
# if cmap is given as string, translate to matplotlib cmap
if isinstance(cmap, str):
cmap = plt.cm.get_cmap(cmap)
# initiate figure and 3d axes
if axes is None:
if figure is None:
figure = plt.figure()
axes = Axes3D(figure, rect=[0, 0, 1, 1], xlim=limits, ylim=limits)
else:
if figure is None:
figure = axes.get_figure()
axes.set_xlim(*limits)
axes.set_ylim(*limits)
axes.view_init(elev=elev, azim=azim)
axes.set_axis_off()
# plot mesh without data
p3dcollec = axes.plot_trisurf(coords[:, 0],
coords[:, 1],
coords[:, 2],
triangles=faces,
linewidth=0.,
antialiased=False,
color='white')
# reduce viewing distance to remove space around mesh
axes.dist = 8
# set_facecolors function of Poly3DCollection is used as passing the
# facecolors argument to plot_trisurf does not seem to work
face_colors = np.ones((faces.shape[0], 4))
if bg_map is None:
bg_data = np.ones(coords.shape[0]) * 0.5
else:
bg_data = load_surf_data(bg_map)
if bg_data.shape[0] != coords.shape[0]:
raise ValueError('The bg_map does not have the same number '
'of vertices as the mesh.')
bg_faces = np.mean(bg_data[faces], axis=1)
if bg_faces.min() != bg_faces.max():
bg_faces = bg_faces - bg_faces.min()
bg_faces = bg_faces / bg_faces.max()
# control background darkness
bg_faces *= darkness
face_colors = plt.cm.gray_r(bg_faces)
# modify alpha values of background
face_colors[:, 3] = alpha * face_colors[:, 3]
# should it be possible to modify alpha of surf data as well?
if surf_map is not None:
surf_map_data = load_surf_data(surf_map)
if surf_map_data.ndim != 1:
raise ValueError('surf_map can only have one dimension but has'
'%i dimensions' % surf_map_data.ndim)
if surf_map_data.shape[0] != coords.shape[0]:
raise ValueError('The surf_map does not have the same number '
'of vertices as the mesh.')
# create face values from vertex values by selected avg methods
if avg_method == 'mean':
surf_map_faces = np.mean(surf_map_data[faces], axis=1)
elif avg_method == 'median':
surf_map_faces = np.median(surf_map_data[faces], axis=1)
# if no vmin/vmax are passed figure them out from data
if vmin is None:
vmin = np.nanmin(surf_map_faces)
if vmax is None:
vmax = np.nanmax(surf_map_faces)
# treshold if inidcated
if threshold is None:
kept_indices = np.arange(surf_map_faces.shape[0])
else:
kept_indices = np.where(np.abs(surf_map_faces) >= threshold)[0]
surf_map_faces = surf_map_faces - vmin
surf_map_faces = surf_map_faces / (vmax - vmin)
# multiply data with background if indicated
if bg_on_data:
face_colors[kept_indices] = cmap(surf_map_faces[kept_indices])\
* face_colors[kept_indices]
else:
face_colors[kept_indices] = cmap(surf_map_faces[kept_indices])
if colorbar:
our_cmap = get_cmap(cmap)
norm = Normalize(vmin=vmin, vmax=vmax)
nb_ticks = 5
ticks = np.linspace(vmin, vmax, nb_ticks)
bounds = np.linspace(vmin, vmax, our_cmap.N)
if threshold is not None:
cmaplist = [our_cmap(i) for i in range(our_cmap.N)]
# set colors to grey for absolute values < threshold
istart = int(norm(-threshold, clip=True) * (our_cmap.N - 1))
istop = int(norm(threshold, clip=True) * (our_cmap.N - 1))
for i in range(istart, istop):
cmaplist[i] = (0.5, 0.5, 0.5, 1.)
our_cmap = LinearSegmentedColormap.from_list(
'Custom cmap', cmaplist, our_cmap.N)
# we need to create a proxy mappable
proxy_mappable = ScalarMappable(cmap=our_cmap, norm=norm)
proxy_mappable.set_array(surf_map_faces)
cax, kw = make_axes(axes,
location='right',
fraction=.1,
shrink=.6,
pad=.0)
cbar = figure.colorbar(proxy_mappable,
cax=cax,
ticks=ticks,
boundaries=bounds,
spacing='proportional',
format='%.2g',
orientation='vertical')
_crop_colorbar(cbar, cbar_vmin, cbar_vmax)
p3dcollec.set_facecolors(face_colors)
if title is not None:
axes.set_title(title, position=(.5, .95))
# save figure if output file is given
if output_file is not None:
figure.savefig(output_file)
plt.close(figure)
else:
return figure
def visualize(samples, labels):
"""Main function.
Note:
First init plots then update per user keypress.
Samples are in the form [(xz, yz, xy), ...] in range [0, common.RADAR_MAX].
Args:
samples (list of tuples of np.array): Data to visualize.
labels (list of strings): Sample labels.
"""
fig = plt.figure()
gs = fig.add_gridspec(2, 2)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
ax3 = fig.add_subplot(gs[1, :])
# Get position maps.
pmap_xz, pmap_yz = gen_pos_map()
idx = 0
# Initial sample.
xz, yz, xy = samples[idx]
title = fig.suptitle(
f'Target Return Signal. Label "{labels[idx]}", Sample {idx}.')
# Setup x-z plane plot.
# Radar target return signal strength in x-z plane.
init_axis(ax1, 'X-Z Plane', 'X (cm)', 'Z (cm)')
sm = ScalarMappable(cmap='coolwarm')
init_c = sm.to_rgba(xz.T.flatten())
pts_xz = ax1.scatter(pmap_xz[0],
pmap_xz[1],
s=pmap_xz[2],
c=init_c,
cmap='coolwarm',
zorder=1)
# Setup y-z plane plot.
# Radar target return signal strength in y-z plane.
init_axis(ax2, 'Y-Z Plane', 'Y (cm)', 'Z (cm)')
sm = ScalarMappable(cmap='coolwarm')
init_c = sm.to_rgba(yz.T.flatten())
pts_yz = ax2.scatter(pmap_yz[0],
pmap_yz[1],
s=pmap_yz[2],
c=init_c,
cmap='coolwarm',
zorder=1)
# Setup x-y plane plot.
# Radar target return signal strength in x-z plane.
init_axis(ax3, 'X-Y Plane', 'X (cm)', 'Y (cm)')
# Calculate axis range to set axis limits and plot extent.
xmax = np.amax(pmap_xz[0]).astype(np.int)
xmin = np.amin(pmap_xz[0]).astype(np.int)
ymax = np.amax(pmap_yz[0]).astype(np.int)
ymin = np.amin(pmap_yz[0]).astype(np.int)
zmax = np.amax(pmap_yz[1]).astype(np.int)
zmin = np.amin(pmap_yz[1]).astype(np.int)
ax3.set_xlim(xmax, xmin)
ax3.set_ylim(ymax, ymin)
# Rotate xy image if radar horizontal since x and y axis are rotated 90 deg CCW.
if RADAR_HORIZONTAL:
xy = np.rot90(xy)
sm = ScalarMappable(cmap='coolwarm')
init_img = sm.to_rgba(xy)
pts_xy = ax3.imshow(init_img,
cmap='coolwarm',
extent=[xmin, xmax, ymin, ymax],
zorder=1)
def update(event):
"""Update plot per keypress."""
nonlocal idx
if event.key == 'n': # next sample
if idx >= len(samples) - 1:
idx = len(samples) - 1
else:
idx += 1
elif event.key == 'b': # prev sample
if idx <= 0:
idx = 0
else:
idx -= 1
elif event.key == 'escape': # all done
plt.close()
return
# Get next sample.
xz, yz, xy = samples[idx]
# Update title.
title.set_text(
f'Target Return Signal. Label "{labels[idx]}", Sample {idx}.')
# Update image colors according to return signal strength on plots.
sm = ScalarMappable(cmap='coolwarm')
pts_xz.set_color(sm.to_rgba(xz.T.flatten()))
sm = ScalarMappable(cmap='coolwarm')
pts_yz.set_color(sm.to_rgba(yz.T.flatten()))
if RADAR_HORIZONTAL:
xy = np.rot90(xy)
sm = ScalarMappable(cmap='coolwarm')
pts_xy.set_data(sm.to_rgba(xy))
# Actual update of plot.
plt.draw()
return
fig.canvas.mpl_connect('key_press_event', update)
plt.show()
return
bin_width = ticks[1] - ticks[0]
bin_centers = np.array(ticks[:-1]) + bin_width / 2
hist_xrange_kwargs = dict(start=min(ticks), end=max(ticks))
else:
levels = MaxNLocator(nbins=config.NBINS).tick_values(
config.MIN_VAL, config.MAX_VAL)
ticks = levels[::3]
cbkwargs = {}
cmkwargs = dict(low=config.MIN_VAL, high=config.MAX_VAL)
bin_width = levels[1] - levels[0]
bin_centers = levels[:-1] + bin_width / 2
hist_xrange_kwargs = dict(start=config.MIN_VAL, end=config.MAX_VAL)
cmap = get_cmap(config.CMAP)
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
sm = ScalarMappable(norm=norm, cmap=cmap)
color_pal = [
RGB(*val).to_hex()
for val in sm.to_rgba(levels, bytes=True, norm=True)[:-1]
]
bin_pal = color_pal.copy()
bin_pal.append('#ffffff')
bin_mapper = BinnedColorMapper(bin_pal, alpha=config.ALPHA)
color_mapper = LinearColorMapper(color_pal, **cmkwargs)
ticker = FixedTicker(ticks=ticks)
cb = ColorBar(color_mapper=color_mapper,
location=(0, 0),
scale_alpha=config.ALPHA,
ticker=ticker,
**cbkwargs)
rasterized=True,
)
for sp in ax.spines.values():
sp.set_visible(False)
plt.xticks([])
plt.yticks([])
plt.title(top_gene)
# colorbars
from matplotlib.colors import Normalize
from matplotlib.cm import ScalarMappable
cbax = fig.add_axes([.93, .6, .01, .2])
plt.colorbar(ScalarMappable(norm=Normalize(), cmap="viridis"),
cax=cbax,
ticks=[-1, 0, 1],
label="Module Score")
cbax = fig.add_axes([.93, .2, .01, .2])
plt.colorbar(
ScalarMappable(norm=Normalize(), cmap=gene_cmap),
cax=cbax,
ticks=[-1, 0, 1],
label="Normalized Log\nExpression",
)
plt.show()
# plt.savefig("Module_Scores.svg", dpi=300)
def show_graph_with_labels(corr_mat,
th,
trps,
halftimes,
prop=None,
path=None,
base_name=None):
# Get a 0-1 matrix for whether corr > threshold
adjacency_matrix = np.zeros(corr_mat.shape)
adjacency_matrix[corr_mat >= th] = 1
adjacency_matrix[corr_mat < th] = 0
rows, cols = np.where(adjacency_matrix == 1)
rows = rows.tolist()
cols = cols.tolist()
edges = []
# keysm.set_array([])
for i in range(len(rows)):
d = {'corr': corr_mat[rows[i], cols[i]]}
e = (trps[rows[i]], trps[cols[i]], d)
edges.append(e)
gr = nx.Graph()
gr.add_edges_from(edges)
edges, corrs = zip(*nx.get_edge_attributes(gr, 'corr').items())
corrs = np.array(corrs)
if prop == None:
pos = nx.kamada_kawai_layout(gr, weight='corr')
else:
pos = {}
for node in gr.nodes():
pos[node] = (halftimes[node]['X'][prop],
halftimes[node]['Y'][prop])
hts = [halftimes[node]['lambda'] for node in gr.nodes()]
hts = np.arcsinh(1 * hts) / 1
node_cmap = plt.cm.coolwarm_r
nodenorm = plt.Normalize(np.min(hts), np.max(hts))
nodesm = ScalarMappable(norm=nodenorm, cmap=node_cmap)
nodesm.set_array([])
low_lim = np.min(corrs)
max_lim = np.max(corrs[corrs < 1.0])
edge_cmap = plt.cm.Greys
edgenorm = plt.Normalize(low_lim, max_lim)
edgesm = ScalarMappable(norm=edgenorm, cmap=edge_cmap)
edgesm.set_array([])
fig = plt.figure(figsize=[10, 6])
nx.draw_networkx(gr,
node_size=100,
with_labels=True,
pos=pos,
alpha=0.9,
node_color=hts,
cmap=node_cmap,
vmin=np.min(hts),
vmax=np.max(hts),
edge_color=corrs,
edge_cmap=edge_cmap,
edge_vmin=low_lim,
edge_vmax=max_lim,
width=1.2,
linewidths=2,
font_size=5,
font_color='black',
font_weight='semibold')
corr_cbar = plt.colorbar(edgesm, orientation="vertical")
corr_cbar.ax.set_title('Correlation')
ht_cbar = plt.colorbar(nodesm, orientation='vertical')
ht_cbar.ax.set_title('Reaction constant')
plt.title('Correlation between tripeptides')
if prop != None:
plt.xlabel('X ' + prop)
plt.ylabel('Z ' + prop)
if path != None and base_name != None:
out_file = path + base_name + '_' + prop + '_graph.png'
plt.savefig(out_file, dpi=400)
else:
plt.show()
return (gr)
zorder=10,
transform=ccrs.PlateCarree())
# Plot buffer that shows where we got cross section stuff from
_ = plot_line_buffer(qlat,
qlon,
delta=epi_area,
zorder=5,
linestyle='--',
linewidth=1.0,
alpha=1.0,
facecolor='none',
edgecolor='k')
# Create colorbar from artifical scalarmappable (alpha is problematic)
c = colorbar(ScalarMappable(cmap=get_cmap('magma'), norm=illumnorm),
orientation='vertical',
aspect=40)
# Set colormap alpha manually
c.solids.set(alpha=0.5)
# ############### Plot section ###################
# Plot section
rfcmap = 'seismic'
figure()
ax = axes(facecolor=(0.8, 0.8, 0.8))
# Plot section
def main(argv):
parser = argparse.ArgumentParser(
description="Plot the loss evolving through time")
parser.add_argument("metrics",
type=file_or_stdin,
help="The file containing the loss")
parser.add_argument("--to_file", help="Save the animation to a video file")
parser.add_argument("--step",
type=int,
default=1000,
help="Change that many datapoints in between frames")
parser.add_argument("--n_points",
type=int,
default=10000,
help="That many points in each frame")
parser.add_argument("--frames",
type=lambda x: slice(*map(maybe_int, x.split(":"))),
default=":",
help="Choose only those frames")
parser.add_argument("--lim",
type=lambda x: map(float, x.split(",")),
help="Define the limits of the axes")
parser.add_argument("--no_colorbar",
action="store_false",
dest="colorbar",
help="Do not display a colorbar")
args = parser.parse_args(argv)
loss = np.loadtxt(args.metrics)
fig, ax = plt.subplots()
lr = LinearRegression()
sc = ax.scatter(loss[:args.n_points, 0],
loss[:args.n_points, 1],
c=colors(loss[:args.n_points, 2]))
lims = args.lim if args.lim else [0, loss[:, 0].max()]
ln, = ax.plot(lims, lims, "--", color="black", label="linear fit")
ax.set_xlim(lims)
ax.set_ylim(lims)
ax.set_xlabel("$L(\cdot)$")
ax.set_ylabel("$\hat{L}(\cdot)$")
if args.colorbar:
mappable = ScalarMappable(cmap="viridis")
mappable.set_array(loss[:10000, 2])
plt.colorbar(mappable)
STEP = args.step
N_POINTS = args.n_points
def update(i):
s = i * STEP
e = s + N_POINTS
lr.fit(loss[s:e, :1], loss[s:e, 1].ravel())
ln.set_ydata([
lr.intercept_.ravel(),
lr.intercept_.ravel() + lims[1] * lr.coef_.ravel()
])
ax.set_title("Showing samples %d to %d" % (s, e))
sc.set_facecolor(colors(loss[s:e, 2]))
sc.set_offsets(loss[s:e, :2])
return ax, sc, ln
anim = FuncAnimation(fig,
update,
interval=100,
frames=np.arange(len(loss) / STEP)[args.frames],
blit=False,
repeat=False)
if args.to_file:
writer = animation_writers["ffmpeg"](fps=15)
anim.save(args.to_file, writer=writer)
else:
plt.show()
def parallel_axes_plot(archive,
ax=None,
bc_order=None,
cmap="magma",
linewidth=1.5,
alpha=0.8,
vmin=None,
vmax=None,
sort_archive=False,
cbar_orientation='horizontal',
cbar_pad=0.1):
"""Visualizes archive entries in behavior space with a parallel axes plot.
This visualization is meant to show the coverage of the behavior space at a
glance. Each axis represents one behavioral dimension, and each line in the
diagram represents one entry in the archive. Three main things are evident
from this plot:
- **Behavior space coverage,** as determined by the amount of the axis that
has lines passing through it. If the lines are passing through all parts
of the axis, then there is likely good coverage for that BC.
- **Correlation between neighboring BCs.** In the below example, we see
perfect correlation between ``behavior_0`` and ``behavior_1``, since none
of the lines cross each other. We also see the perfect negative
correlation between ``behavior_3`` and ``behavior_4``, indicated by the
crossing of all lines at a single point.
- **Whether certain values of the behavior dimensions affect the objective
value strongly.** In the below example, we see ``behavior_2`` has many
entries with high objective near zero. This is more visible when
``sort_archive`` is passed in, as entries with higher objective values
will be plotted on top of individuals with lower objective values.
Examples:
.. plot::
:context: close-figs
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from ribs.archives import GridArchive
>>> from ribs.visualize import parallel_axes_plot
>>> # Populate the archive with the negative sphere function.
>>> archive = GridArchive(
... [20, 20, 20, 20, 20],
... [(-1, 1), (-1, 1), (-1, 1), (-1, 1), (-1, 1)]
... )
>>> archive.initialize(solution_dim=3)
>>> for x in np.linspace(-1, 1, 100):
... for y in np.linspace(0, 1, 100):
... for z in np.linspace(-1, 1, 100):
... archive.add(
... solution=np.array([x,y,z]),
... objective_value=-(x**2 + y**2 + z**2),
... behavior_values=np.array([0.5*x,x,y,z,-0.5*z]),
... )
>>> # Plot a heatmap of the archive.
>>> plt.figure(figsize=(8, 6))
>>> parallel_axes_plot(archive)
>>> plt.title("Negative sphere function")
>>> plt.ylabel("axis values")
>>> plt.show()
Args:
archive (ArchiveBase): Any ribs archive.
ax (matplotlib.axes.Axes): Axes on which to create the plot.
If None, the current axis will be used.
bc_order (list of int or list of (int, str)): If this is a list of ints,
it specifies the axes order for BCs (e.g. ``[2, 0, 1]``). If this is
a list of tuples, each tuple takes the form ``(int, str)`` where the
int specifies the BC index and the str specifies a name for the BC
(e.g. ``[(1, "y-value"), (2, "z-value"), (0, "x-value)]``). The
order specified does not need to have the same number of elements as
the number of behaviors in the archive, e.g. ``[1, 3]`` or
``[1, 2, 3, 2]``.
cmap (str, list, matplotlib.colors.Colormap): Colormap to use when
plotting intensity. Either the name of a colormap, a list of RGB or
RGBA colors (i.e. an Nx3 or Nx4 array), or a colormap object.
linewidth (float): Line width for each entry in the plot.
alpha (float): Opacity of the line for each entry (passing a low value
here may be helpful if there are many archive entries, as more
entries would be visible).
vmin (float): Minimum objective value to use in the plot. If None, the
minimum objective value in the archive is used.
vmax (float): Maximum objective value to use in the plot. If None, the
maximum objective value in the archive is used.
sort_archive (boolean): if true, sorts the archive so that the highest
performing entries are plotted on top of lower performing entries.
.. warning:: This may be slow for large archives.
cbar_orientation (str): The orientation of the colorbar. Use either
``'vertical'`` or ``'horizontal'``
cbar_pad (float): The amount of padding to use for the colorbar.
Raises:
ValueError: ``cbar_orientation`` has an invalid value.
ValueError: The bcs provided do not exist in the archive.
TypeError: bc_order is not a list of all ints or all tuples.
"""
# Try getting the colormap early in case it fails.
cmap = _retrieve_cmap(cmap)
# Check that the orientation input is correct.
if cbar_orientation not in ['vertical', 'horizontal']:
raise ValueError("cbar_orientation mus be 'vertical' or 'horizontal' "
f"but is '{cbar_orientation}'")
# If there is no order specified, plot in increasing numerical order.
if bc_order is None:
cols = [f"behavior_{i}" for i in range(archive.behavior_dim)]
axis_labels = cols
lower_bounds = archive.lower_bounds
upper_bounds = archive.upper_bounds
# Use the requested behaviors (may be less than the original number of bcs).
else:
# Check for errors in specification.
if all(isinstance(bc, int) for bc in bc_order):
bc_indices = np.array(bc_order)
axis_labels = [f"behavior_{i}" for i in bc_indices]
elif all(
len(bc) == 2 and isinstance(bc[0], int)
and isinstance(bc[1], str) for bc in bc_order):
bc_indices, axis_labels = zip(*bc_order)
bc_indices = np.array(bc_indices)
else:
raise TypeError("bc_order must be a list of ints or a list of"
"tuples in the form (int, str)")
if np.max(bc_indices) >= archive.behavior_dim:
raise ValueError(f"Invalid Behavior: requested behavior index "
f"{np.max(bc_indices)}, but archive only has "
f"{archive.behavior_dim} behaviors.")
if any(bc < 0 for bc in bc_indices):
raise ValueError("Invalid Behavior: requested a negative behavior"
" index.")
# Find the indices of the requested order.
cols = [f"behavior_{i}" for i in bc_indices]
lower_bounds = archive.lower_bounds[bc_indices]
upper_bounds = archive.upper_bounds[bc_indices]
host_ax = plt.gca() if ax is None else ax # Try to get current axis.
df = archive.as_pandas(include_solutions=False)
if sort_archive:
df.sort_values(by=['objective'], inplace=True)
vmin = np.min(df['objective']) if vmin is None else vmin
vmax = np.max(df['objective']) if vmax is None else vmax
norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax, clip=True)
objectives = df['objective'].to_numpy()
ys = df[cols].to_numpy()
y_ranges = upper_bounds - lower_bounds
# Transform all data to be in the first axis coordinates.
normalized_ys = np.zeros_like(ys)
normalized_ys[:, 0] = ys[:, 0]
normalized_ys[:, 1:] = (
(ys[:, 1:] - lower_bounds[1:]) / y_ranges[1:] * y_ranges[0] +
lower_bounds[0])
# Copy the axis for the other bcs.
axes = [host_ax] + [host_ax.twinx() for i in range(len(cols) - 1)]
for i, axis in enumerate(axes):
axis.set_ylim(lower_bounds[i], upper_bounds[i])
axis.spines['top'].set_visible(False)
axis.spines['bottom'].set_visible(False)
if axis != host_ax:
axis.spines['left'].set_visible(False)
axis.yaxis.set_ticks_position('right')
axis.spines["right"].set_position(("axes", i / (len(cols) - 1)))
host_ax.set_xlim(0, len(cols) - 1)
host_ax.set_xticks(range(len(cols)))
host_ax.set_xticklabels(axis_labels)
host_ax.tick_params(axis='x', which='major', pad=7)
host_ax.spines['right'].set_visible(False)
host_ax.xaxis.tick_top()
for archive_entry, objective in zip(normalized_ys, objectives):
# Draw straight lines between the axes in the appropriate color.
color = cmap(norm(objective))
host_ax.plot(range(len(cols)),
archive_entry,
c=color,
alpha=alpha,
linewidth=linewidth)
# Create a colorbar.
mappable = ScalarMappable(cmap=cmap)
mappable.set_clim(vmin, vmax)
host_ax.figure.colorbar(mappable,
pad=cbar_pad,
orientation=cbar_orientation)
def trajectory(
adata: AnnData,
basis: str = "umap",
root_milestone: Union[str, None] = None,
milestones: Union[str, None] = None,
color_seg: str = "t",
cmap_seg: str = "viridis",
layer_seg: Union[str, None] = "fitted",
perc_seg: Union[List, None] = None,
color_cells: Union[str, None] = None,
scale_path: float = 1,
arrows: bool = False,
arrow_offset: int = 10,
show_info: bool = True,
ax=None,
show: Optional[bool] = None,
save: Union[str, bool, None] = None,
**kwargs,
):
"""\
Project trajectory onto embedding.
Parameters
----------
adata
Annotated data matrix.
basis
Name of the `obsm` basis to use.
root_milestone
tip defining progenitor branch.
milestones
tips defining the progenies branches.
col_seg
color trajectory segments.
layer_seg
layer to use when coloring seg with a feature.
perc_seg
percentile cutoffs for segments.
color_cells
cells color.
scale_path
changes the width of the path
arrows
display arrows on segments (positioned at half pseudotime distance).
arrow_offset
arrow offset in number of nodes used to obtain its direction.
show_info
display legend/colorbar.
ax
Add plot to existing ax
show
show the plot.
save
save the plot.
kwargs
arguments to pass to :func:`scanpy.pl.embedding`.
Returns
-------
If `show==False` a :class:`~matplotlib.axes.Axes`
"""
class GC(GraphicsContextBase):
def __init__(self):
super().__init__()
self._capstyle = "round"
def custom_new_gc(self):
return GC()
RendererBase.new_gc = types.MethodType(custom_new_gc, RendererBase)
if "graph" not in adata.uns:
raise ValueError(
"You need to run `tl.pseudotime` first before plotting.")
graph = adata.uns["graph"]
emb = adata.obsm[f"X_{basis}"]
emb_f = adata[graph["cells_fitted"], :].obsm[f"X_{basis}"]
if "components" in kwargs:
cmp = np.array(kwargs["components"]) - 1
emb_f = emb_f[:, cmp]
else:
emb_f = emb_f[:, :2]
R = graph["R"]
nodes = graph["pp_info"].index
proj = pd.DataFrame((np.dot(emb_f.T, R) / R.sum(axis=0)).T, index=nodes)
B = graph["B"]
g = igraph.Graph.Adjacency((B > 0).tolist(), mode="undirected")
g.vs[:]["name"] = [v.index for v in g.vs]
miles_ids = np.concatenate([graph["tips"], graph["forks"]])
if root_milestone is not None:
dct = graph["milestones"]
nodes = g.get_all_shortest_paths(dct[root_milestone],
[dct[m] for m in milestones])
nodes = np.unique(np.concatenate(nodes))
tips = graph["tips"][np.isin(graph["tips"], nodes)]
proj = proj.loc[nodes, :]
g.delete_vertices(
graph["pp_info"].index[~graph["pp_info"].index.isin(nodes)])
if ax is None:
ax = sc.pl.embedding(adata, show=False, basis=basis, **kwargs)
else:
sc.pl.embedding(adata, show=False, ax=ax, basis=basis, **kwargs)
c_edges = np.array([e.split("|") for e in adata.obs.edge], dtype=int)
cells = [any(np.isin(c_e, nodes)) for c_e in c_edges]
import logging
anndata_logger = logging.getLogger("anndata")
prelog = anndata_logger.level
anndata_logger.level = 40
adata_c = adata[cells, :]
if is_categorical(adata, color_cells):
if (color_cells + "_colors" not in adata.uns
or len(adata.uns[color_cells + "_colors"]) == 1):
from . import palette_tools
palette_tools._set_default_colors_for_categorical_obs(
adata, color_cells)
adata_c.uns[color_cells + "_colors"] = [
adata.uns[color_cells + "_colors"][np.argwhere(
adata.obs[color_cells].cat.categories == c)[0][0]]
for c in adata_c.obs[color_cells].cat.categories
]
if ax is None:
ax = sc.pl.embedding(adata,
color=color_cells,
basis=basis,
show=False,
**kwargs)
else:
sc.pl.embedding(adata,
color=color_cells,
basis=basis,
ax=ax,
show=False,
**kwargs)
anndata_logger.level = prelog
if show_info == False and color_cells is not None:
if is_categorical(adata, color_cells):
if ax.get_legend() is not None:
ax.get_legend().remove()
else:
ax.set_box_aspect(aspect=1)
fig = ax.get_gridspec().figure
cbar = np.argwhere(
["colorbar" in a.get_label() for a in fig.get_axes()]).ravel()
if len(cbar) > 0:
fig.get_axes()[cbar[0]].remove()
al = np.array(g.get_edgelist())
edges = [g.vs[e.tolist()]["name"] for e in al]
lines = [[tuple(proj.loc[j]) for j in i] for i in edges]
vals = pd.Series(_get_color_values(adata, color_seg, layer=layer_seg)[0],
index=adata.obs_names)
R = pd.DataFrame(adata.uns["graph"]["R"],
index=adata.uns["graph"]["cells_fitted"])
R = R.loc[adata.obs_names]
vals = vals[~np.isnan(vals)]
R = R.loc[vals.index]
def get_nval(i):
return np.average(vals, weights=R.loc[:, i])
node_vals = np.array(list(map(get_nval, range(R.shape[1]))))
seg_val = node_vals[np.array(edges)].mean(axis=1)
if perc_seg is not None:
min_v, max_v = np.percentile(seg_val, perc_seg)
seg_val[seg_val < min_v] = min_v
seg_val[seg_val > max_v] = max_v
sm = ScalarMappable(norm=Normalize(vmin=seg_val.min(), vmax=seg_val.max()),
cmap=cmap_seg)
lines = [lines[i] for i in np.argsort(seg_val)]
seg_val = seg_val[np.argsort(seg_val)]
lc = matplotlib.collections.LineCollection(lines,
colors="k",
linewidths=7.5 * scale_path,
zorder=100)
ax.add_collection(lc)
g = igraph.Graph.Adjacency((B > 0).tolist(), mode="undirected")
seg = graph["pp_seg"].loc[:, ["from", "to"]].values.tolist()
if arrows:
for s in seg:
path = np.array(g.get_shortest_paths(s[0], s[1])[0])
coord = proj.loc[path, ].values
out = np.empty(len(path) - 1)
cdist_numba(coord, out)
mid = np.argmin(np.abs(out.cumsum() - out.sum() / 2))
if mid + arrow_offset > (len(path) - 1):
arrow_offset = len(path) - 1 - mid
ax.quiver(
proj.loc[path[mid], 0],
proj.loc[path[mid], 1],
proj.loc[path[mid + arrow_offset], 0] - proj.loc[path[mid], 0],
proj.loc[path[mid + arrow_offset], 1] - proj.loc[path[mid], 1],
headwidth=15 * scale_path,
headaxislength=10 * scale_path,
headlength=10 * scale_path,
units="dots",
zorder=101,
)
c_arrow = vals[(np.array([e.split("|") for e in adata.obs.edge],
dtype=int) == path[mid]).sum(
axis=1) == 1].mean()
ax.quiver(
proj.loc[path[mid], 0],
proj.loc[path[mid], 1],
proj.loc[path[mid + arrow_offset], 0] - proj.loc[path[mid], 0],
proj.loc[path[mid + arrow_offset], 1] - proj.loc[path[mid], 1],
headwidth=12 * scale_path,
headaxislength=10 * scale_path,
headlength=10 * scale_path,
units="dots",
color=sm.to_rgba(c_arrow),
zorder=102,
)
lc = matplotlib.collections.LineCollection(
lines,
colors=[sm.to_rgba(sv) for sv in seg_val],
linewidths=5 * scale_path,
zorder=104,
)
miles_ids = miles_ids[np.isin(miles_ids, proj.index)]
ax.scatter(
proj.loc[miles_ids, 0],
proj.loc[miles_ids, 1],
zorder=103,
c="k",
s=200 * scale_path,
)
ax.add_collection(lc)
tip_val = node_vals[miles_ids]
ax.scatter(
proj.loc[miles_ids, 0],
proj.loc[miles_ids, 1],
zorder=105,
c=sm.to_rgba(tip_val),
s=140 * scale_path,
)
if show == False:
return ax
savefig_or_show("trajectory", show=show, save=save)
def cvt_archive_heatmap(archive,
ax=None,
plot_centroids=True,
plot_samples=False,
transpose_bcs=False,
cmap="magma",
square=False,
ms=1,
lw=0.5,
vmin=None,
vmax=None):
"""Plots heatmap of a :class:`~ribs.archives.CVTArchive` with 2D behavior
space.
Essentially, we create a Voronoi diagram and shade in each cell with a
color corresponding to the objective value of that cell's elite.
Depending on how many bins are in the archive, ``ms`` and ``lw`` may need to
be tuned. If there are too many bins, the Voronoi diagram and centroid
markers will make the entire image appear black. In that case, try turning
off the centroids with ``plot_centroids=False`` or even removing the lines
completely with ``lw=0``.
Examples:
.. plot::
:context: close-figs
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from ribs.archives import CVTArchive
>>> from ribs.visualize import cvt_archive_heatmap
>>> # Populate the archive with the negative sphere function.
>>> archive = CVTArchive(100, [(-1, 1), (-1, 1)])
>>> archive.initialize(solution_dim=2)
>>> for x in np.linspace(-1, 1, 100):
... for y in np.linspace(-1, 1, 100):
... archive.add(solution=np.array([x,y]),
... objective_value=-(x**2 + y**2),
... behavior_values=np.array([x,y]))
>>> # Plot a heatmap of the archive.
>>> plt.figure(figsize=(8, 6))
>>> cvt_archive_heatmap(archive)
>>> plt.title("Negative sphere function")
>>> plt.xlabel("x coords")
>>> plt.ylabel("y coords")
>>> plt.show()
Args:
archive (CVTArchive): A 2D CVTArchive.
ax (matplotlib.axes.Axes): Axes on which to plot the heatmap. If None,
the current axis will be used.
plot_centroids (bool): Whether to plot the cluster centroids.
plot_samples (bool): Whether to plot the samples used when generating
the clusters.
transpose_bcs (bool): By default, the first BC in the archive will
appear along the x-axis, and the second will be along the y-axis. To
switch this (i.e. to transpose the axes), set this to True.
cmap (str, list, matplotlib.colors.Colormap): Colormap to use when
plotting intensity. Either the name of a colormap, a list of RGB or
RGBA colors (i.e. an Nx3 or Nx4 array), or a colormap object.
square (bool): If True, set the axes aspect ratio to be "equal".
ms (float): Marker size for both centroids and samples.
lw (float): Line width when plotting the voronoi diagram.
vmin (float): Minimum objective value to use in the plot. If None, the
minimum objective value in the archive is used.
vmax (float): Maximum objective value to use in the plot. If None, the
maximum objective value in the archive is used.
Raises:
ValueError: The archive is not 2D.
"""
# pylint: disable = too-many-locals
if archive.behavior_dim != 2:
raise ValueError("Cannot plot heatmap for non-2D archive.")
# Try getting the colormap early in case it fails.
cmap = _retrieve_cmap(cmap)
# Retrieve data from archive.
lower_bounds = archive.lower_bounds
upper_bounds = archive.upper_bounds
centroids = archive.centroids
samples = archive.samples
if transpose_bcs:
lower_bounds = np.flip(lower_bounds)
upper_bounds = np.flip(upper_bounds)
centroids = np.flip(centroids, axis=1)
samples = np.flip(samples, axis=1)
# Retrieve and initialize the axis.
ax = plt.gca() if ax is None else ax
ax.set_xlim(lower_bounds[0], upper_bounds[0])
ax.set_ylim(lower_bounds[1], upper_bounds[1])
if square:
ax.set_aspect("equal")
# Add faraway points so that the edge regions of the Voronoi diagram are
# filled in. Refer to
# https://stackoverflow.com/questions/20515554/colorize-voronoi-diagram
# for more info.
interval = upper_bounds - lower_bounds
scale = 1000
faraway_pts = [
upper_bounds + interval * scale, # Far upper right.
upper_bounds + interval * [-1, 1] * scale, # Far upper left.
lower_bounds + interval * [-1, -1] * scale, # Far bottom left.
lower_bounds + interval * [1, -1] * scale, # Far bottom right.
]
vor = Voronoi(np.append(centroids, faraway_pts, axis=0))
# Calculate objective value for each region. `vor.point_region` contains
# the region index of each point.
region_obj = [None] * len(vor.regions)
min_obj, max_obj = np.inf, -np.inf
pt_to_obj = _get_pt_to_obj(archive)
for pt_idx, region_idx in enumerate(
vor.point_region[:-4]): # Exclude faraway_pts.
if region_idx != -1 and pt_idx in pt_to_obj:
obj = pt_to_obj[pt_idx]
min_obj = min(min_obj, obj)
max_obj = max(max_obj, obj)
region_obj[region_idx] = obj
# Override objective value range.
min_obj = min_obj if vmin is None else vmin
max_obj = max_obj if vmax is None else vmax
# Shade the regions.
for region, objective in zip(vor.regions, region_obj):
# This check is O(n), but n is typically small, and creating
# `polygon` is also O(n) anyway.
if -1 not in region:
if objective is None:
color = "white"
else:
normalized_obj = np.clip(
(objective - min_obj) / (max_obj - min_obj), 0.0, 1.0)
color = cmap(normalized_obj)
polygon = [vor.vertices[i] for i in region]
ax.fill(*zip(*polygon), color=color, ec="k", lw=lw)
# Create a colorbar.
mappable = ScalarMappable(cmap=cmap)
mappable.set_clim(min_obj, max_obj)
ax.figure.colorbar(mappable, ax=ax, pad=0.1)
# Plot the sample points and centroids.
if plot_samples:
ax.plot(samples[:, 0], samples[:, 1], "o", c="gray", ms=ms)
if plot_centroids:
ax.plot(centroids[:, 0], centroids[:, 1], "ko", ms=ms)
def plot_roi_summary(roi_pac_all,
reject,
xaxis,
marker_size,
eventnames,
lobe_bounds,
lobe_names,
ordered_rois,
yl_factor=1,
ylim=None):
# roi_pac_all: (1 x Nlevels x Nrois) or ( Nsubjects x Nlevels x Nrois)
# reject: (1 x Nlevels x Nrois) or ( Nsubjects x Nlevels x Nrois)
# xaxis: (1 x Nrois) or (Nsubjects x Nrois)
Nlevels = len(eventnames)
if ylim is None:
ylim = np.nanmax(np.abs(roi_pac_all)) * 1.1 * np.array([-1, 1])
cmap = plt.get_cmap('seismic')
norm = Normalize(vmin=ylim[0], vmax=ylim[1])
scalarMap = ScalarMappable(norm=norm, cmap=cmap)
fig = plt.figure()
fig.clf()
fig.set_size_inches([17, 9.5])
gs = gridspec.GridSpec(Nlevels + 1,
1,
height_ratios=[yl_factor] * (Nlevels - 1) +
[1, 0.5],
hspace=0)
axs = []
for level_i, level_name in enumerate(eventnames):
ax = fig.add_subplot(gs[level_i])
toplot_level = roi_pac_all[:, level_i, :]
mask_level = reject[:, level_i, :]
ax.scatter(xaxis[mask_level],
toplot_level[mask_level],
s=marker_size,
c=toplot_level[mask_level],
cmap=cmap,
norm=norm,
marker='o',
edgecolors='k',
zorder=3)
ax.scatter(xaxis[np.logical_not(mask_level)],
toplot_level[np.logical_not(mask_level)],
s=marker_size,
c='k',
cmap=cmap,
norm=norm,
marker='+',
zorder=3)
ax.axhline(0, color='k')
for lobe, next in zip(lobe_bounds[:-1], lobe_bounds[1:]):
ax.axvline((lobe[1] + next[0]) / 2, color='k')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_xticks(xaxis[0, :])
if level_i == Nlevels - 1:
ax.set_xticklabels(ordered_rois, rotation='vertical')
else:
ax.set_xticklabels('')
hdiff = (xaxis[0, 1] - xaxis[0, 0])
ax.set_xlim([xaxis[0, 0] - hdiff, xaxis[0, -1] + hdiff])
ax.set_ylabel(level_name)
if level_i < Nlevels - 1:
ax.set_ylim(ylim * yl_factor)
else:
ax.set_ylim(ylim)
if level_i == 0:
for lobe, name in zip(lobe_bounds, lobe_names):
ax.text(np.mean(lobe),
ax.get_ylim()[1] * 1.1,
name,
horizontalalignment='center')
axs.append(ax)
return fig, axs, gs
import sys
import numpy as np
#maps = ["Accent","Accent_r","Blues","Blues_r","BrBG","BrBG_r","BuGn","BuGn_r","BuPu","BuPu_r","CMRmap","CMRmap_r","Dark2","Dark2_r","GnBu","GnBu_r","Greens","Greens_r","Greys","Greys_r","OrRd","OrRd_r","Oranges","Oranges_r","PRGn","PRGn_r","Paired","Paired_r","Pastel1","Pastel1_r","Pastel2","Pastel2_r","PiYG","PiYG_r","PuBu","PuBuGn","PuBuGn_r","PuBu_r","PuOr","PuOr_r","PuRd","PuRd_r","Purples","Purples_r","RdBu","RdBu_r","RdGy","RdGy_r","RdPu","RdPu_r","RdYlBu","RdYlBu_r","RdYlGn","RdYlGn_r","Reds","Reds_r","Set1","Set1_r","Set2","Set2_r","Set3","Set3_r","Spectral","Spectral_r","Vega10","Vega10_r","Vega20","Vega20_r","Vega20b","Vega20b_r","Vega20c","Vega20c_r","Wistia","Wistia_r","YlGn","YlGnBu","YlGnBu_r","YlGn_r","YlOrBr","YlOrBr_r","YlOrRd","YlOrRd_r","afmhot","afmhot_r","autumn","autumn_r","binary","binary_r","bone","bone_r","brg","brg_r","bwr","bwr_r","cool","cool_r","coolwarm","coolwarm_r","copper","copper_r","cubehelix","cubehelix_r","flag","flag_r","gist_earth","gist_earth_r","gist_gray","gist_gray_r","gist_heat","gist_heat_r","gist_ncar","gist_ncar_r","gist_rainbow","gist_rainbow_r","gist_stern","gist_stern_r","gist_yarg","gist_yarg_r","gnuplot","gnuplot2","gnuplot2_r","gnuplot_r","gray","gray_r","hot","hot_r","hsv","hsv_r","inferno","inferno_r","jet","jet_r","magma","magma_r","nipy_spectral","nipy_spectral_r","ocean","ocean_r","pink","pink_r","plasma","plasma_r","prism","prism_r","rainbow","rainbow_r","seismic","seismic_r","spectral","spectral_r","spring","spring_r","summer","summer_r","terrain","terrain_r","viridis","viridis_r","winter","winter_r"]
maps = ["afmhot","CMRmap","flag","cubehelix","Blues_r","bone","BrBG","BrBG_r","BuGn_r","binary_r","coolwarm","Spectral","Spectral_r","terrain","gist_stern","gnuplot","hot","inferno","plasma","prism","Reds","seismic"]
for cmp in maps:
# Color mapping
from matplotlib.cm import ScalarMappable
mycmap = ScalarMappable(cmap=cmp)
#mycmap.to_rgba([0,1])
from PIL import Image, ImageDraw
image_grey = Image.open(sys.argv[1])
image_grey.load()
image_grey = image_grey.convert('L')
image = Image.fromarray(np.uint8(np.array(mycmap.to_rgba(image_grey.getdata())).reshape(*(image_grey.size),4)*255))
filename = sys.argv[1].split('.')[0]+'_color_'+cmp+'.bmp'
print('Saving', filename)
image.save(filename, "BMP")
'''image = Image.new("RGB", image_grey.size, (0,0,0))
draw = ImageDraw.Draw(image)
for i in range(image_grey.size[0]):
for j in range(image_grey.size[0]):
c = image_grey.getpixel((i, j))[0]
clr = mycmap.to_rgba(c/255)
draw.point((i, j), (int(255*clr[0]), int(255*clr[1]), int(255*clr[2])))'''
def plot_projection_surfaces(projections,
eventnames,
vertices,
subject,
subjects_dir,
savename,
Nprojs=3,
blnOffscreen=False,
savepath=None):
'''
Plot the source space projections of the frequency profiles
See Figure 3 in Stephen et al 2019
:param projections: ndarray, (components,levels,sources)
The projections to plot, e.g. from spatial_phase_amplitude_coupling.compute_source_space_projections
:param eventnames: list of basestring
List of names of the levels
:param vertices: list of numeric
the vertices corresponding to the sources in projections
:param subject: basestr
The subject ID
:param subjects_dir:
The subject directory
:param savename:
The name of the file to save to
:param Nprojs: int
The number of projections to plot
:param blnOffscreen: bool
Whether to plot the surfaces offscreen
:param savepath: basestr | None
The path to save to (or None for no save)
:return:
'''
def label_func(f):
return eventnames[int(f)]
for proji in range(Nprojs):
toplot = projections[proji, :, :]
toplot[np.isnan(toplot)] = 0
yl_diff = np.array([-1, 1]) * np.percentile(
np.abs(toplot[~np.isnan(toplot)]), 100)
ctrl_pts = [yl_diff[0], 0, yl_diff[1]]
stc_proj = mne.SourceEstimate(toplot.T,
vertices=vertices,
tmin=0,
tstep=1,
subject=subject)
brain = plot_surf(stc_proj, {
'kind': 'value',
'lims': ctrl_pts
},
'seismic',
label_func,
'semi-inflated',
False,
blnOffscreen,
subjects_dir=subjects_dir,
blnMaskUnknown=True)
cmap = plt.get_cmap('seismic')
norm = Normalize(vmin=ctrl_pts[0], vmax=ctrl_pts[-1])
scalarMap = ScalarMappable(norm=norm, cmap=cmap)
scalarMap.set_array(np.linspace(ctrl_pts[0], ctrl_pts[-1], 100))
if savepath is not None:
fname = savename + '_FreqProfs_surfProj{}'.format(proji + 1)
for j, (t, level) in enumerate(zip(stc_proj.times, eventnames)):
brain.set_time(t)
save_surf(brain, fname, savepath, '_{}{}'.format(j, level))
brain.close()
def multiscatter(deamid_mat,
key=None,
type=None,
hts=None,
l=None,
t=None,
path=None,
base_name=None,
low_counts=None,
**kwargs):
"""
Creates tripetides multiscatter plot
Input:
- key: Ydata entry info to include in the plot
- l: index of layer to plot
- type: wether key is categorical ('cat') or continuous ('cont')
- path: path in which plot is saved
*Notes:
- key can refer to numeric but still categorical data
"""
if l is not None:
Ddata = deamid_mat.layers[l]
else:
Ddata = deamid_mat.D
counts = deamid_mat.counts
Ydata = deamid_mat.Ydata
trps_data = deamid_mat.trps_data
if key is not None:
Yvect = Ydata[key]
else:
Yvect = np.zeros(Ddata.shape[0])
if len(hts) == 0:
exists_hts = False
else:
exists_hts = True
# Detect samples with known Ydata
if type == 'cat':
known = Yvect != 'U'
elif type == 'cont':
known = Yvect != -1
else:
known = np.array([True for i in range(len(Yvect))])
# Apply transformation to continuous data in order to visualize better
if type == 'cont' and t != None:
Yvect = transform(Yvect, t)
# Include visual options from vopts to defaults
dfts = {
'fontsize': 12,
'fontpos': [0.15, 0.35],
'fontweight': 'medium',
'mcolor': 'black',
'msize': 5,
'reg': None,
'cat_cmap': 'tab10',
'bbox_to_anchor': (0.5, 2.2)
}
dfts.update(kwargs)
# Create mapping for either continuous or categorical data
if type == 'cat':
Yset = np.sort(list(set(Yvect))) # Set (array type) of Y values
keyCm = plt.get_cmap(dfts['cat_cmap'])
keyNorm = plt.Normalize(vmin=0, vmax=len(Yset))
keySm = ScalarMappable(norm=keyNorm, cmap=keyCm)
keySm.set_array([])
# Handles for the legned
handles = [
plt.plot([], [],
markersize=18,
marker='.',
ls='none',
c=keySm.to_rgba(v))[0] for v in range(len(Yset))
]
plt.close()
map_color = np.zeros(Yvect.shape[0])
i = 0
for Yval in Yset:
map_color[Yvect == Yval] = i
i += 1
Yvect = map_color
elif type == 'cont':
# Create mappable for colorbar for ages
keyCm = plt.get_cmap("cool")
keyNorm = plt.Normalize(np.min(Yvect[known]), np.max(Yvect[known]))
keySm = ScalarMappable(norm=keyNorm, cmap=keyCm)
keySm.set_array([])
else:
keyCm = plt.get_cmap("brg")
keyNorm = plt.Normalize(0, 1)
keySm = ScalarMappable(norm=keyNorm, cmap=keyCm)
keySm.set_array([])
# Create map for halftime
if exists_hts == True:
htcm = plt.get_cmap("coolwarm")
htnorm = plt.Normalize(np.min(hts), np.max(hts))
htsm = ScalarMappable(norm=htnorm, cmap=htcm)
htsm.set_array([])
num_dims = Ddata.shape[1]
fig = plt.figure(figsize=(16, 12))
axes = [[False for i in range(num_dims)] for j in range(num_dims)]
n = 1
if dfts['reg'] == 'linear':
reg = linear_model.BayesianRidge(fit_intercept=False)
for i in range(num_dims):
for j in range(num_dims):
ax = fig.add_subplot(num_dims, num_dims, n)
# Extract j and i columns
plot_data = Ddata[:, [j, i]]
# Check for pairwise low counts data rowwise
if low_counts is not None:
counts_data = counts[:, [j, i]]
not_miss = counts_data > low_counts
else:
not_miss = ~np.isnan(plot_data)
sele = np.sum(not_miss, 1) == 2
sele = np.array(sele)
X = plot_data[:, 0]
Y = plot_data[:, 1]
# Yplot = Yvect[sele]
sele_known = np.logical_and(sele, known)
sele_unknown = np.logical_and(sele, ~known)
if i != j:
# Fit linear regression
if X.shape[0] > 1 and dfts['reg'] is not None:
x_pred = np.linspace(np.nanmin(X), np.nanmax(X), 500)
if dfts['reg'] == 'linear':
# Linear 1to1
reg.fit(X[sele].reshape(-1, 1), Y[sele])
y_pred = reg.predict(x_pred.reshape(-1, 1))
pl = ax.plot(x_pred.reshape(-1, 1),
y_pred,
color='lime',
linewidth=1)
# pl = ax.plot([0,1], [0,1], color='lime', linewidth=0.6, alpha=0.8)
else:
# Non-linear regression 1 parameter a
a0 = 1
res1 = least_squares(lsf1par,
a0,
loss='soft_l1',
f_scale=0.1,
args=(X, Y))
a = res1.x
y_pred = np.exp(a * (x_pred - 1))
pl = ax.plot(x_pred,
y_pred,
color='black',
linewidth=1)
# Non-linear regression 1 parameter a
a0 = 1
res2 = least_squares(lsf1par_inv,
a0,
loss='soft_l1',
f_scale=0.1,
args=(X, Y))
a = res2.x
x_pred1 = np.linspace(0.0001, 1.1, 500)
y_pred = np.log(x_pred1) / a + 1
pl = ax.plot(x_pred1, y_pred, color='red', linewidth=1)
if type != None:
sc = ax.scatter(X[sele_known],
Y[sele_known],
s=dfts['msize'],
alpha=0.7,
c=keySm.to_rgba(Yvect[sele_known]))
sc = ax.scatter(X[sele_unknown],
Y[sele_unknown],
s=dfts['msize'],
alpha=0.7,
c='black')
else:
# rel = X/Y
# rel = np.logical_and(rel>0.8, rel<1.2)
# rel = rel*1
# sc = ax.scatter(X, Y, s=dfts['msize'], c=keySm.to_rgba(rel), alpha=0.8)
sc = ax.scatter(X[sele_known],
Y[sele_known],
s=dfts['msize'],
c=dfts['mcolor'],
alpha=0.8)
elif i == j:
txt = trps_data[i]['tripep']
if trps_data[i][1] != 'NA':
txt = trps_data[i][0] + '\n' + txt
if trps_data[i][4] != 0:
txt = txt + str(trps_data[i][4])
elif trps_data[i][3] != 0:
txt = txt + str(trps_data[i][3])
ax.text(dfts['fontpos'][0],
dfts['fontpos'][1],
txt,
fontweight=dfts['fontweight'],
fontsize=dfts['fontsize'],
transform=ax.transAxes)
if exists_hts == False:
ax.set_facecolor('white')
elif exists_hts == True and hts[i] != -1:
ax.set_facecolor(htsm.to_rgba(hts[i]))
else:
ax.set_facecolor('white')
ax.axis(xmin=np.nanmin(X),
xmax=np.nanmax(X),
ymin=np.nanmin(Y),
ymax=np.nanmax(Y))
# Set axis labels:
ax.set_xlabel(trps_data['tripep'][j])
ax.set_ylabel(trps_data['tripep'][i])
# Equal scale axis
# ax.axis(xmin=-0.1, xmax=1.1, ymin=-0.1, ymax=1.1)
# ax.set_aspect(aspect=1)
# Hide axes for all but the plots on the edge:
if i < num_dims - 1:
ax.xaxis.set_visible(False)
if j > 0:
ax.yaxis.set_visible(False)
# Add this axis to the list.
axes[j][i] = ax
n += 1
axes = np.array(axes)
plt.subplots_adjust(left=0.1,
right=0.9,
top=0.85,
bottom=0.1,
wspace=0.2,
hspace=0.2)
# halftime color bar
if exists_hts != False:
htcbar_ax = fig.add_axes([0.92, 0.51, 0.015, 0.38])
htcbar = plt.colorbar(htsm, cax=htcbar_ax, orientation="vertical")
htcbar.ax.set_title("Reaction\nconstant")
if type == 'cont': # Make a colorbar for it
keycb_ax = fig.add_axes([0.92, 0.1, 0.015, 0.38])
keycbar = plt.colorbar(keysm, cax=keycb_ax, orientation="vertical")
if t != None:
keycbar.ax.set_title(key + ' ({})'.format(t))
else:
keycbar.ax.set_title(key)
elif type == 'cat': # Set the legend for it
# ax_pos =int(np.floor(num_dims/2))
# axes[ax_pos,0].legend(handles, Yset,ncol=3,
# bbox_to_anchor=dfts['bbox_to_anchor'],
# fontsize='large')
leg = plt.legend(handles,
Yset,
ncol=3,
bbox_to_anchor=dfts['bbox_to_anchor'],
fontsize='large')
export_legend(leg, key)
# Show or save
if path is None and base_name is None:
plt.show()
else:
plt.savefig(path + base_name + '_multiscatter.png',
dpi=300,
format=None)
plt.close()
def _get_2d_plot(self,
label_stable=True,
label_unstable=True,
ordering=None,
energy_colormap=None,
vmin_mev=-60.0,
vmax_mev=60.0,
show_colorbar=True,
process_attributes=False):
"""
Shows the plot using pylab. Usually I won't do imports in methods,
but since plotting is a fairly expensive library to load and not all
machines have matplotlib installed, I have done it this way.
"""
plt = get_publication_quality_plot(8, 6)
from matplotlib.font_manager import FontProperties
if ordering is None:
(lines, labels, unstable) = self.pd_plot_data
else:
(_lines, _labels, _unstable) = self.pd_plot_data
(lines, labels,
unstable) = order_phase_diagram(_lines, _labels, _unstable,
ordering)
if energy_colormap is None:
if process_attributes:
for x, y in lines:
plt.plot(x, y, "k-", linewidth=3, markeredgecolor="k")
# One should think about a clever way to have "complex"
# attributes with complex processing options but with a clear
# logic. At this moment, I just use the attributes to know
# whether an entry is a new compound or an existing (from the
# ICSD or from the MP) one.
for x, y in labels.iterkeys():
if labels[(x, y)].attribute is None or \
labels[(x, y)].attribute == "existing":
plt.plot(x,
y,
"ko",
linewidth=3,
markeredgecolor="k",
markerfacecolor="b",
markersize=12)
else:
plt.plot(x,
y,
"k*",
linewidth=3,
markeredgecolor="k",
markerfacecolor="g",
markersize=18)
else:
for x, y in lines:
plt.plot(x,
y,
"ko-",
linewidth=3,
markeredgecolor="k",
markerfacecolor="b",
markersize=15)
else:
from matplotlib.colors import Normalize, LinearSegmentedColormap
from matplotlib.cm import ScalarMappable
pda = PDAnalyzer(self._pd)
for x, y in lines:
plt.plot(x, y, "k-", linewidth=3, markeredgecolor="k")
vmin = vmin_mev / 1000.0
vmax = vmax_mev / 1000.0
if energy_colormap == 'default':
mid = -vmin / (vmax - vmin)
cmap = LinearSegmentedColormap.from_list(
'my_colormap', [(0.0, '#005500'), (mid, '#55FF55'),
(mid, '#FFAAAA'), (1.0, '#FF0000')])
else:
cmap = energy_colormap
norm = Normalize(vmin=vmin, vmax=vmax)
_map = ScalarMappable(norm=norm, cmap=cmap)
_energies = [
pda.get_equilibrium_reaction_energy(entry)
for coord, entry in labels.iteritems()
]
energies = [en if en < 0.0 else -0.00000001 for en in _energies]
vals_stable = _map.to_rgba(energies)
ii = 0
if process_attributes:
for x, y in labels.iterkeys():
if labels[(x, y)].attribute is None or \
labels[(x, y)].attribute == "existing":
plt.plot(x,
y,
"o",
markerfacecolor=vals_stable[ii],
markersize=12)
else:
plt.plot(x,
y,
"*",
markerfacecolor=vals_stable[ii],
markersize=18)
ii += 1
else:
for x, y in labels.iterkeys():
plt.plot(x,
y,
"o",
markerfacecolor=vals_stable[ii],
markersize=15)
ii += 1
font = FontProperties()
font.set_weight("bold")
font.set_size(24)
# Sets a nice layout depending on the type of PD. Also defines a
# "center" for the PD, which then allows the annotations to be spread
# out in a nice manner.
if len(self._pd.elements) == 3:
plt.axis("equal")
plt.xlim((-0.1, 1.2))
plt.ylim((-0.1, 1.0))
plt.axis("off")
center = (0.5, math.sqrt(3) / 6)
else:
all_coords = labels.keys()
miny = min([c[1] for c in all_coords])
ybuffer = max(abs(miny) * 0.1, 0.1)
plt.xlim((-0.1, 1.1))
plt.ylim((miny - ybuffer, ybuffer))
center = (0.5, miny / 2)
plt.xlabel("Fraction", fontsize=28, fontweight='bold')
plt.ylabel("Formation energy (eV/fu)",
fontsize=28,
fontweight='bold')
for coords in sorted(labels.keys(), key=lambda x: -x[1]):
entry = labels[coords]
label = entry.name
# The follow defines an offset for the annotation text emanating
# from the center of the PD. Results in fairly nice layouts for the
# most part.
vec = (np.array(coords) - center)
vec = vec / np.linalg.norm(vec) * 10 if np.linalg.norm(vec) != 0 \
else vec
valign = "bottom" if vec[1] > 0 else "top"
if vec[0] < -0.01:
halign = "right"
elif vec[0] > 0.01:
halign = "left"
else:
halign = "center"
if label_stable:
if process_attributes and entry.attribute == 'new':
plt.annotate(latexify(label),
coords,
xytext=vec,
textcoords="offset points",
horizontalalignment=halign,
verticalalignment=valign,
fontproperties=font,
color='g')
else:
plt.annotate(latexify(label),
coords,
xytext=vec,
textcoords="offset points",
horizontalalignment=halign,
verticalalignment=valign,
fontproperties=font)
if self.show_unstable:
font = FontProperties()
font.set_size(16)
pda = PDAnalyzer(self._pd)
energies_unstable = [
pda.get_e_above_hull(entry)
for entry, coord in unstable.iteritems()
]
if energy_colormap is not None:
energies.extend(energies_unstable)
vals_unstable = _map.to_rgba(energies_unstable)
ii = 0
for entry, coords in unstable.items():
vec = (np.array(coords) - center)
vec = vec / np.linalg.norm(vec) * 10 \
if np.linalg.norm(vec) != 0 else vec
label = entry.name
if energy_colormap is None:
plt.plot(coords[0],
coords[1],
"ks",
linewidth=3,
markeredgecolor="k",
markerfacecolor="r",
markersize=8)
else:
plt.plot(coords[0],
coords[1],
"s",
linewidth=3,
markeredgecolor="k",
markerfacecolor=vals_unstable[ii],
markersize=8)
if label_unstable:
plt.annotate(latexify(label),
coords,
xytext=vec,
textcoords="offset points",
horizontalalignment=halign,
color="b",
verticalalignment=valign,
fontproperties=font)
ii += 1
if energy_colormap is not None and show_colorbar:
_map.set_array(energies)
cbar = plt.colorbar(_map)
cbar.set_label(
'Energy [meV/at] above hull (in red)\nInverse energy ['
'meV/at] above hull (in green)',
rotation=-90,
ha='left',
va='center')
ticks = cbar.ax.get_yticklabels()
cbar.ax.set_yticklabels([
'${v}$'.format(v=float(t.get_text().strip('$')) * 1000.0)
for t in ticks
])
f = plt.gcf()
f.set_size_inches((8, 6))
plt.subplots_adjust(left=0.09, right=0.98, top=0.98, bottom=0.07)
return plt
R_Nfig.savefig(out_dir + base_name + '_loadings_Ri_N.svg', format='svg')
plt.close()
R_Qfig.savefig(out_dir + base_name + '_loadings_Ri_Q.svg', format='svg')
plt.close()
# -------------------------------------------------------------------
# PCA PLOTS AND REGRESSION
Yvect = merged_deamid_mat.Ydata['Substrate']
Yset = set(Yvect)
Yset = np.sort(list(Yset))
# Map to color
keyCm = plt.get_cmap('tab20')
keyNorm = plt.Normalize(vmin=0, vmax=2 * len(Yset))
keySm = ScalarMappable(norm=keyNorm, cmap=keyCm)
keySm.set_array([])
map_color = {}
i = 1
for Yval in Yset:
map_color[Yval] = keySm.to_rgba(i)
i += 1
# -------------------------------------------------------------------
### REGRESSION USING PC1 on Thermal AGE WITH BONE AND
# TAR SEEP SAMPLES
# Get known age samples
ages = merged_deamid_mat.Ydata['10C Thermal age'].astype('float')
mask = np.logical_or(Yvect == 'Bone', Yvect == 'Tar seep bone')
mask = np.logical_or(mask, Yvect == 'Dental calculus')
ages_b = ages[mask]
coadmin = df['coadmin'] + np.random.rand(n)
inter = df['inter'] + np.random.rand(n)
norm = mpl.colors.Normalize(vmin=0, vmax=80)
darkred = '#d62728'
lightred = '#fae9e9'
darkblue = '#1f77b4'
lightblue = '#e8f1f7'
cmap_female = LinearSegmentedColormap.from_list('female',
[lightred, darkred])
cmap_male = LinearSegmentedColormap.from_list('male',
[lightblue, darkblue])
cmap_gray = LinearSegmentedColormap.from_list('gray',
['#f2f2f2', '#7f7f7f'])
scmap_female = ScalarMappable(norm=norm, cmap=cmap_female)
scmap_male = ScalarMappable(norm=norm, cmap=cmap_male)
scmap_gray = ScalarMappable(norm=norm, cmap=cmap_gray)
cmap_female.set_over(darkred)
cmap_male.set_over(darkblue)
scmap_male.set_array(df['age'].values)
scmap_female.set_array(df['age'].values)
scmap_gray.set_array(df['age'].values)
#
df['color'] = df.swifter.apply(calc_color_per_gender_and_age,
axis='columns',
args=(
scmap_female,
